Asymmetric Heterogeneous Transfer Learning: A Survey

Magda Friedjungov´a and Marcel Jiˇrina

Faculty of Information Technology, Czech Technical University in Prague, Czech Republic

Keywords:

Asymmetric Heterogeneous Transfer Learning, Different Feature Space, Domain Adaptation, Survey, Data

Mining, Metric Learning.

Abstract:

One of the main prerequisites in most machine learning and data mining tasks is that all available data origi-

nates from the same domain. In practice, we often can’t meet this requirement due to poor quality, unavailable

data or missing data attributes (new task, e.g. cold-start problem). A possible solution can be the combi-

nation of data from different domains represented by different feature spaces, which relate to the same task.

We can also transfer the knowledge from a different but related task that has been learned already. Such a

solution is called transfer learning and it is very helpful in cases where collecting data is expensive, difﬁcult

or impossible. This overview focuses on the current progress in the new and unique area of transfer learning

- asymmetric heterogeneous transfer learning. This type of transfer learning considers the same task solved

using data from different feature spaces. Through suitable mappings between these different feature spaces

we can get more data for solving data mining tasks. We discuss approaches and methods for solving this type

of transfer learning tasks. Furthermore, we mention the most used metrics and the possibility of using metric

or similarity learning.

1 INTRODUCTION

It can happen that while solving a data mining task

in some domain of interest we will have data avail-

able from different feature spaces. Different feature

spaces are two spaces, which are represented by dif-

ferent features. These spaces can originate from other

domains and we can divide them into source (usu-

ally contains data used for the training of the model)

and target feature space (contains data used for testing

of the model). We can also divide the domains into

source and target to distinguish where the data comes

from. Common machine learning methods assume

that the distributions of the individual attributes in the

used data are the same. However, in practice these as-

sumptions are often incorrect. Let us imagine that our

task is object classiﬁcation, where our domain is rep-

resented by several pictures. Two images of the same

object may be of different dimensions of features be-

cause of different resolutions, illuminations or tilts. A

different task can be cross-lingual document classiﬁ-

cation. For example labeled English documents are

widely available, but labeled Chinese documents are

much harder to obtain. These documents, English and

Chinese, do not share the same feature representation.

Transfer learning can use natural correspondence be-

tween feature spaces in order to create an automated

learner for Chinese documents. Transfer learning al-

lows the domains, tasks and distributions used in solv-

ing tasks to be different (Zheng, 2015).

In a broader context transfer learning can be re-

garded as a group of methods which fall into the cate-

gory of semantic-meaning based methods for cross-

domain data fusion. Data fusion consists of tech-

niques for integration of knowledge from various data

in a machine learning and data mining task (Zheng,

2015). Transfer learning concerns itself with knowl-

edge fusion rather than schema mapping and data

merging, which are more speciﬁc for traditional data

fusion and data integration being pursued by the

database community (Zheng, 2015; Bleiholder and

Naumann, 2009). Many terms exist for transfer learn-

ing, within this work you can also come across a re-

lated term - domain adaptation (Pan and Yang, 2009).

Domain adaptation is focused on the development of

learning algorithms, which can be easily transferred

from one domain to another (Daum´e III, 2009).

A general overview of transfer learning is given

in (Pan and Yang, 2009) and the newest survey was

introduced in (Weiss et al., 2016). The main moti-

vation, which is also the reason that transfer learning

methods are popular, is the automatization of map-

ping processes, saving of time and human resources,

the possibility of solving tasks without knowledge of

Friedjungová, M. and Ji

rina, M.

Asymmetric Heterogeneous Transfer Learning: A Survey.

DOI: 10.5220/0006396700170027

In Proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA 2017), pages 17-27

ISBN: 978-989-758-255-4

the domain, increasing usability of poor data, which

would be unusable on its own and often also the us-

ability of data from unused database structures.

In this survey we focus on the diversity of data

from different feature spaces in the same domain of

interest. We search for suitable mappings between

this data, which will maintain or decrease the error

of the predictive or classiﬁcation model. In practice,

the mapping of data is often solved manually, but in

some cases this approach poses a combinatorial prob-

lem and almost always requires the presence of a do-

main expert. In the ideal case it would be beneﬁcial to

ﬁnd such an automatic mapping, which would enable

us to map the data between source and target feature

spaces in both directions. This research area is called

heterogeneous transfer learning and its position in the

ﬁeld of transfer learning can be seen in Figure 1.

Heterogeneous transfer learning can be perceived

as a type of transductive learning. Transductive learn-

ing assumes that the source and target domains are

different. The domain consists of two components: a

feature space χ and a marginalprobability distribution

P(X), where X = x

, ..., x

∈ χ (Pan and Yang, 2009).

Heterogeneous transfer learning is proposed to handle

the cases where the task is the same, but the source

and target feature spaces are different (Pan and Yang,

2009). This difference can be that the marginal prob-

ability distributions of the data are different, but the

feature spaces between source and target domains are

the same. Transductive learning also assumes that we

havelabeled source data and unlabeled target data, but

heterogeneous transfer learning is able to work with

different combinations of labeled and unlabeled data

which will be demonstrated on examples in Section 2.

In this paper we focus on feature-based heterogeneous

transfer learning, which stems from the assumption

that the feature spaces between domains are different

and searches for ways of mapping this dissimilar data.

By difference we understand different distributions,

representations and dimensionality of data. Hetero-

geneous transfer learning is a relatively new ﬁeld of

research and ﬁnds an application in such domains as

text classiﬁcation, image recognition, activity recog-

nition, defect prediction etc.

There has been a large amount of work focus-

ing on transfer learning in machine learning literature

(See an overview by (Weiss et al., 2016)). However,

in this surveyarticle we givean overviewof asymmet-

ric heterogeneous transfer learning methods mainly

used in machine learning and data mining areas. This

survey does not provide an experimental comparison

of the individual methods. Most of the methods in the

surveyed papers are domain or task speciﬁc, thus they

reach the best performance on speciﬁc datasets. This

makes it impossible to provide a quality comparative

analysis on the same datasets. An open-source reposi-

tory of implemented solutions from each paper would

be helpful, but unfortunately this is not available. We

hope to provide a useful survey for the machine learn-

ing and data mining community.

The survey is organized as follows. In Section

2 you can ﬁnd an explanation of this problem along

with the prerequisites for the problem of transfer

learning between different feature spaces. Section 2.1

brieﬂy describes the solution based on common fea-

ture space. In section 2.2 we explain the available

methods in more detail. In Section 3 we introduce the

reader to the most used feature mappings in the ﬁeld

of transfer learning. In the last Section 4 we bring a

brief summary of this survey and possible challenges,

which we would like to address in the future.

2 HETEROGENEOUS TRANSFER

LEARNING

In Figure 2 (altered ﬁgure from (Weiss et al., 2016))

we can see two approaches to the transformation of

data on a feature-based level, which are addressed by

different feature spaces. By transformation we un-

derstand operations (e.g. translation, scaling, etc.)

which have to be done for mapping of different fea-

ture spaces. One of these is symmetric transformation

(Figure 2.a). Symmetric transformation transforms

the source and target feature spaces into a common

latent feature space in order to unify the input spaces

of the domains. The second approach is asymmetric

transformation (Figure 2.b), which transforms source

feature space to the target feature space. Some meth-

ods presented in Section 2.2 perform the transforma-

tion in the opposite direction from the target domain

to the source domain. Some proposed methods are

usable in both directions. All presented approaches in

Section 2.2 are based on features (feature-based) (Pan

and Yang, 2009).

We consider two datasets as a running example

in this paper (shown on Figure 3). The data in these

datasets originate from different source and target do-

mains with different feature spaces. The datasets

consist of a different feature representation, distribu-

tion, scale, and density of data. Relations which con-

nect the datasets can exist between different feature

spaces, because the source and target domains must

be related in some way. These connections can be

called correspondences among features. The discov-

ery of as much common information as possible from

these different datasets is one of the problems in data

mining research. Thus, we are looking for suitable

DATA 2017 - 6th International Conference on Data Science, Technology and Applications

Transfer Learning

Heterogeneous

Transfer Learning

Homogeneous

Transfer Learning

Instance-based Relational-basedParameter-based

Inductive Learning Transductive Learning

Feature-based Hybrid-based

Symmetric Asymmetric

Feature-based

Symmetric Asymmetric

other domains

with same

feature space

same domain

with different

feature spaces

How is being transferred

What is being transferred

Label-based

Figure 1: A hierarchical overview of different transfer learning approaches.

Asymmetric Heterogeneous Transfer Learning: A Survey

Figure 2: (a) Symmetric transformation mapping T

and T

of the source X

and target X

domains into a common latent

feature space. (b) Asymmetric transformation mapping T

of the source domain X

to target domain X

or vice versa from

target X

to source X

domain.

mapping functions for individual features from dif-

ferent feature spaces. In the ideal scenario, we are

looking for mapping in both directions, which would

be more general and would ﬁnd a wide range of ap-

plication.

2.1 Symmetric Feature-based Approach

Most existing heterogeneous transfer learning meth-

ods assume that both source and target data can

be represented by a common homogeneous feature

space, called latent feature space. Thus, we are look-

ing for transformation mappings T

and T

to trans-

form source domain data X

and target domain data

to a new common latent feature space X

as is

shown in Figure 2 (Shi et al., 2010; Prettenhofer and

Stein, 2010; Wang and Mahadevan, 2011; Duan et al.,

2012). There exist a lot of tasks in the natural lan-

guage processing area. (Blitzer et al., 2006) introduce

structural correspondence learning (SCL) to automat-

ically induce correspondences among features from

different domains. SCL is based on the correlation

between certain pivot features. Pivot features are fea-

tures which behave in the same way for discrimina-

tive learning (e.g. classiﬁcation) in both domains.

These features have the same semantic meaning in

both domains and are used to learn a mapping from

the different feature spaces to common latent feature

space. (Pan et al., 2008) learn a common latent fea-

ture space with low dimensionality. This common la-

tent space is learned using a new dimensionality re-

duction method called Maximum Mean Discrepancy

Embedding. Data from related but different domains

is projected onto this common latent feature space.

(Daum´e III, 2009) transform the source and target fea-

tures into a higher dimensional representation with

source, target and common components. They also

introduce an extension to this method which also

works for unlabeled data (Daum´e III et al., 2010).

(Shi et al., 2010) propose the Heterogeneous Spec-

tral Mapping (HeMap) method. The main idea of

HeMap is to ﬁnd a common latent feature space for

two heterogeneous tasks. A spectral mapping of the

source and target feature spaces into a common latent

feature space is designed. A spectral mapping is de-

signed as an optimization task that maintains the orig-

inal structure of the data while minimizing the differ-

ence between the two domains. This mapping adopts

linear approaches such as rotation, scaling, permuta-

tion of row vectors and column vectors, etc. to ﬁnd

a common latent feature space. (Pan et al., 2008) in-

troduce Transfer Component Analysis for dimension-

ality reduction of features in a common latent space.

A kernel-based feature mapping method has been in-

troduced by (Zhong et al., 2009). This method maps

the marginal distribution of data from different source

and target domains into common kernel space. A lot

of methods for the solution of the common latent fea-

ture space problem exist. We are not going to concern

ourselves with them any further in this work, because

we focus on asymmetric approaches.

2.2 Asymmetric Feature-based

Approach

Asymmetric transformation is perceived as a new and

unique approach alternative to symmetric transforma-

tion. It consists of ﬁnding transformations T

, which

would enable us to map source domain data X

target domain data X

with different feature spaces

(Kulis et al., 2011; Dai et al., 2008). In practice we

mostly encounter this version of the problem: we have

data from a source domain, which we map to data

from a target domain using various techniques. Af-

ter that we train a model (Figure 3 a). We can also

encounter a scenario where we have source and target

data from the same domain, on which we trained a

very good model. This model is successfully applied

in production. However, due to various reasons, the

target feature space changed and our model became

unable to react to the new data. In the case that we

are not able to modify the model, we have to ﬁnd an

ideal mapping of target data to source while making

sure that the error of the model doesn’t change (Fig-

ure 3 b). This approach poses a big challenge in the

research area.

DATA 2017 - 6th International Conference on Data Science, Technology and Applications

Dataset A

Source Feature Space Target Feature Space

Dataset B

Model

Transformation of source feature

space to target feature space

Step 1

Step 2

Step 3

(a)

Dataset A

Source Feature Space Target Feature Space

Dataset B

Model

Testing phase

(prediction, classification)

Testing phase

(prediction,

classification)

Transformation of target feature

space to source feature space

Step 1

Step 2

Step 3

(b)

Transformed

dataset A

Transformed

dataset B

Translators,

mapping methods,

metric and similarity

learning etc.

Translators,

mapping methods,

metric and similarity

learning etc.

Training phase

(building of model)

Training phase

(building of model)

Figure 3: There are two approaches in asymmetric transfer learning shown on (a) and (b). (a): Step 1 consists of mapping

dataset A to dataset B by using mapping methods. Step 2 consists of training the model based on data from the transformed

dataset A, step 3 contains the phase where we test the model on dataset B from target feature space. (b): Step 1 consists of the

training of the model based on data from dataset A from source feature space. In Step 2 we are looking for a mapping from

dataset B to dataset A. Step 3 shows the testing of the model based on transformed dataset B.

Asymmetric Heterogeneous Transfer Learning: A Survey

2.3 Overview of Presented Solutions

Asymmetric heterogeneous transfer learning ﬁnds ap-

plication in several practical domains, where mainly

methods of machine learning are employed. In this

section we will introduce several basic research ap-

proaches to solving tasks related to:

• computer vision – image classiﬁcation;

• cross-language classiﬁcation;

• cross-project defect prediction;

• activity recognition.

We start with the assumption that we have differ-

ent source and target feature spaces, but the same task,

as shown on Figure 3. However, if we also had a dif-

ferent task, we would ﬁrst make use of the methods of

transfer between feature spaces followed by methods

of homogeneous transfer learning for the transfer of

knowledge between tasks (domains).

(Kulis et al., 2011) solved the problem of domain

adaptation for transferring object models from one

dataset to another by introducing a novel approach in

computer vision. The main idea is to learn an asym-

metric non-linear transformation that maps data from

one domain to another domain using supervised data

from both domains. The input consists of pairs of

inter-domain examples that are known to be semanti-

cally similar or dissimilar. This approach can also be

used in the case that some classes in the target domain

have missing labels. (Kulis et al., 2011) aims to gen-

eralize the model of (Saenko et al., 2010) in his pa-

per, which makes use of symmetric transformations.

The new approach of (Kulis et al., 2011) was called

Asymmetric Regularized Cross-domain transforma-

tion, shortly ARC-t. ARC-t shows how a general for-

mulation for the transfer learning problem can be ap-

plied in kernel space, resulting in non-linear transfor-

mations. The transformation matrix is learned in a

non-linear Gaussian RBF kernel space. The result-

ing algorithm is based on squared Frobenius regular-

ization (Kulis et al., 2013) and similarity constraints.

Similarity constraints are created for all pairs of data

in the same class by using a similarity function. It

helps us to decide which pairs of data are similar and

dissimilar. During testing the method showed certain

advantages compared to existing baseline approaches

such as k-nearest neighbors, SVM, domain and cross-

domain metric learnings and the feature augmentation

method proposed by (Daum´e III, 2009). The main

idea of feature augmentation is to duplicate features

in order to capture differences among domains. This

method is brieﬂy discussed in Section 2.1.

(Zhou et al., 2014b) proposed a domain adaptation

method where data originates from heterogeneous

feature spaces of different dimensions. The method

includes a transformation matrix to map the weight

vector of classiﬁers learned from source domain to

target domain. This method works if the following re-

quirements are met: sparse feature representation and

class-invariant transformation. Sparse feature repre-

sentation means that a feature in one domain can be

represented by only several features in a different do-

main. The feature mapping across these domains is

linear. Class-invariant transformation means that the

feature mapping of some feature is invariant to dif-

ferent classes. To make the learning of a heteroge-

neous domain possible, the transformation matrix has

to learn the similarity between source and target do-

main data. This data can be transformed from source

feature space to target feature space and equivalently

vice versa. (Zhou et al., 2014b) used the scheme -

the Error Correcting Output Codes (ECOC) to gener-

ate binary classiﬁers for the multi-class classiﬁcation

problem (Dietterich and Bakiri, 1995). With ECOC,

their solution, called Sparse Heterogeneous Feature

Representation (SHFR), can learn a transformation

matrix. Part of the learning process of the transfor-

mation matrix is the adoption of a multi-task learn-

ing method based on (Ando and Zhang, 2005). The

multi-task learning is based on learning more task si-

multaneously (Pan and Yang, 2009). SHFR method

(also in combination with ECOC) was tested against

DAMA (Dai et al., 2008), ARC-t (Kulis et al., 2011)

and HFA (Duan et al., 2012).

We are further going to state several examples

from practice. Heterogeneous transfer learning ﬁnds

numerous applications in the area of activity recogni-

tion. In this area the activities of daily living are mon-

itored through diverse sensors. This monitoring is a

crucial step in the future of elderly people care. There

is motivation to use existing data from other houses in

order to learn the parameters of the model for a new

house. The reason is that activity recognition mod-

els often rely on labeled examples of activities for

learning, which are missing in a new house. Activ-

ity recognition and discovery models usually include

information based on structural, temporal and also

spatial features of the activities (Cook et al., 2013;

Rashidi and Cook, 2010).

For the activity recognition task, (van Kasteren

et al., 2010; van Kasteren et al., 2008) introduce a

method in which sensors in the source domain are

mapped to similar sensors in the target domain. Semi-

supervised learning is used for learning parameters

of the Hidden Markov Model (HMM). They propose

a number of manual mapping strategies for mapping

sensors between different houses: intersect, duplicate,

union.

DATA 2017 - 6th International Conference on Data Science, Technology and Applications

(Rashidi and Cook, 2010) also map sensors from

source to target domain based on location or function.

Their method is called Multi Home Transfer Learning

(MHTL). MHTL composes of 3 phases – activity ex-

traction (output heads to activity templates), mapping

and label assignment. The activity model consists

of various features from sensors, such as structural,

spatial and temporal. Their method is a good exam-

ple of the utilization of meta-features. Meta-features

are common for all data. It is a kind of mapping

allowing us to have a single common feature space

that can be used for all houses (van Kasteren et al.,

2010). In (Rashidi and Cook, 2010) meta-features are

ﬁrst manually introduced into the feature space (for

every source-target pair). Then this feature space is

automatically mapped from the source to target do-

main. Other works using meta-features are (Blanke

and Schiele, 2010; van Kasteren et al., 2010).

(Harel and Mannor, 2011) designed a Multiple

Outlook Mapping algorithm (MOMAP). MOMAP

computes optimal afﬁne mappings from different

source feature spaces (in their terminology outlooks)

to a target feature space by matching moments of

empirical distributions. These spaces are not related

through corresponding instances, but only through the

common task. The optimal afﬁne mapping is a func-

tion of geometric projection which maps the points

lying on the same line onto one common point or

to the same number of other points. Afﬁne map-

ping preserves the division ratio (Nomizu and Sasaki,

1995). In the MOMAP algorithm afﬁne mapping is

represented by translation and rotation. The mapping

is done by matching moments of empirical distribu-

tions. The empirical distribution function is associ-

ated with an empirical measure of samples. Empirical

measure means random measure realization of a se-

quence of random variables. The moments are quan-

tiles from the empirical distribution function (van der

Vaart, 2000). Before mapping in the MOMAP al-

gorithm, the scaling of features of all spaces is re-

quired. The scaling aims to normalize the features

of all spaces to the same range. Then the mapping

is performed by translating the means of each class

to zero. Next, the rotation of the classes to ﬁt each

other is done by a rotation matrix. Finally, we have to

translate the means of the mapped space to the ﬁnal

space. The framework performance is demonstrated

on activity recognition data.

A lot of transfer learning methods solve situa-

tions where the difference between the source and

target domains is caused mainly by differences in

the marginal probability distributions of the domains

(Rashidi and Cook, 2010; Blitzer et al., 2007; Blitzer

et al., 2006). By marginal probability distribution

we mean probability distribution of the features con-

tained in the subset of a collection of random features.

(Feuz and Cook, 2015) propose a novel hetero-

geneous transfer learning technique called Feature-

Space Remapping (FSR). FSR can handle the differ-

ent feature spaces without the use of co-occurrence

data (correlated data), as is shown for example in

(Dai et al., 2008). FSR maps the data to a different

feature space using part of labeled data from the tar-

get domain to infer relations to the source domain.

FSR requires a one time manual speciﬁcation of meta-

features and then can be applied to map multiple

source and target domains. (Feuz and Cook, 2015)

map the features from target feature space to differ-

ent source feature space. To achieve the feature map-

ping, they learn a mapping from each dimension in

the target feature space to a corresponding dimension

in the source feature space. FSR computes the av-

erage similarity between the source and target meta-

feature values for each pair between features from

source and target feature spaces. The similarity is

calculated between two meta-features as the absolute

value of the difference between meta-feature values

divided by the maximum possible difference between

the meta-features. As a product we get many-to-one

mapping.

One of the typical tasks of transfer learning is

language transformation between multilingual doc-

uments or web pages, speciﬁcally the transforma-

tion from one language to another. This task can

be solved by using automatic translators mentioned

in (Wei and Pal, 2010). Transfer learning is aim-

ing to solve the task without these tools, only us-

ing the transfer of knowledge between related feature

spaces. Most methods learn a feature mapping be-

tween source and target domain based on data cor-

respondences (Kulis et al., 2011; Dai et al., 2008).

Correspondence means that there exists some rela-

tionships between data from different feature spaces.

(Zhou et al., 2014a) present a hybrid heterogeneous

transfer learning (HHTL) framework. HHTL consists

of deep learning which learns a feature mapping from

the target domain to the source domain. HHTL simul-

taneously corrects the data bias on the mapped feature

space. This frameworkwas tested on multilingual text

mining tasks.

(Dai et al., 2008) propose to learn feature map-

ping based on the construction of feature correspon-

dences between different feature spaces. This con-

struction is called translator in (Dai et al., 2008). In

this work the language model is used. The language

model is a probability distribution over sequences of

words (Lafferty and Zhai, 2001). The language model

links the class labels to the features in the source

Asymmetric Heterogeneous Transfer Learning: A Survey

spaces and turns their translation to features in the

target spaces. This novel framework is called trans-

lated learning (TLRisk). The main idea of translated

learning is to translate all the data from source feature

space into a target feature space. We assume there

is no correspondence between instances in these dif-

ferent feature spaces. The language model proposed

by (Dai et al., 2008) consists of feature translation and

nearest neighbor learning. We can imagine this model

as a chain of links which is modeled using a Markov

chain and risk minimization. For the development of

a translator we need some co-occurrence data across

source and target spaces. Performance of the TLRisk

framework was shown on text-aided image classiﬁca-

tion and on cross-language classiﬁcation.

Software defect prediction is another important

application area in transfer learning and software en-

gineering. There is a possibility to build a predic-

tion model with defect data collected from a software

project and predict defects for new projects (cross-

project defect prediction CPDP) (Nam et al., 2013;

He et al., 2012; Ma et al., 2012; Rahman et al.,

2012). However, projects must have the same metric

set with identical meanings between projects. (Nam

and Kim, 2015) introduce heterogeneous defect pre-

diction (HDP) which allows heterogeneous metric

sets across projects. At ﬁrst they apply feature se-

lection techniques to the source data. For feature se-

lection they used various feature selection approaches

widely used in defect prediction such as gain ratio,

chi-square, relief-F, and signiﬁcance attribute evalu-

ation. Then the similarity between source and tar-

get data is computed and data is mapped. The sim-

ilarity of each source and target metric pair is mea-

sured by using several existing methods such as per-

centiles, Kolmogorov-Smirnov test and Spearman’s

correlation coefﬁcient. A model for target labels pre-

diction is built using the mapped data.

3 FEATURE MAPPINGS

The main operation of transfer learning approaches

is the mapping of features. These features originate

from different feature spaces. We can map the fea-

tures into common feature space or we can look for

mapping from one feature space to another. Learning

optimal mapping is a signiﬁcant problem in the ma-

chine learning community. By mapping we mean a

type of feature transformation which maps features

from one feature space to another. We face many

problems while mapping one feature space onto an-

other: different number of features, dimensionality,

distribution, used metrics etc. The number of possible

mappings between source and target spaces grows ex-

ponentially as the number of features increases. The

selection of a suitable mapping depends on the type

of data – numerical (each data instance is a numerical

feature vector) or structured (each instance is a struc-

tured object such as a string, a tree etc.), and on the

speciﬁc problem. If the number of feature combina-

tions doesn’t pose a combinatorial problem, it is pos-

sible to realize feature mapping in a manual way. (van

Kasteren et al., 2008) proposed a number of man-

ual mapping strategies: intersect, duplicate, union.

But manual mapping can be domain dependent as is

shown in (van Kasteren et al., 2008). (van Kasteren

et al., 2008) implemented their mapping solution for

different sensors in house activity recognition.

State-of-art of feature mapping consists of pre-

processing, dimensionality reduction and feature se-

lection methods. There exist works, which concern

themselves with dimensionality reduction (Si et al.,

2010; Dai et al., 2009; Pan et al., 2008; Blitzer et al.,

2006) and feature selection (Satpal and Sarawagi,

2007). We can also encounter feature weighting

methods. But a wide spectrum of the methods cov-

ers the preprocessing phase. In this work several

mapping approaches used within transfer learning are

presented. We can divide them into statistic and

metric methods. Statistic methods are represented

primarily in the Spearman’s correlation coefﬁcient,

Kolmogorov-Smirnov test, Kullback-Leibler diver-

gence etc. We will focus more on pairwise metrics

which are based on measuring the distance or simi-

larity between data. As stated above, each problem

pertains to a speciﬁc domain which has its own se-

mantic notion of data similarity. This data similarity

can be difﬁcult to express through standard metrics

such as Minkowski metrics (Bellet et al., 2013). The

solution seems to be learning the metric from data.

This approach is called metric learning or similarity

learning(Bellet et al., 2013; Kulis et al., 2013; Yang,

2006).

3.1 Metrics

There exist a lot of metrics, which can be used (tested)

during the mapping of features. The fundamental one

is the family of Minkowski distances including Eu-

clidean, Manhattan and Chebyshev distances. We can

also use cosine similarity for measuring the cosine of

the angle between two instances. It is widely used in

data mining (e.g. bag-of-words or for sparse vectors).

One of the popular methods for comparing structured

data is standard (Levenshtein) edit distance and its

mutations (e.g. Speciﬁc Cost Matrix, tree or stochas-

tic edit distance), where we search for the smallest

number of transformations (insertion, deletion or sub-

DATA 2017 - 6th International Conference on Data Science, Technology and Applications

stitution of symbols), which is needed to transform

one string to another (Bellet et al., 2013). Many met-

rics exist as well as some good surveys (Bellet et al.,

2013; Kulis et al., 2013).

3.2 Metric and Similarity Learning

Similarity learning is a ﬁeld of supervised learning

and its task is to discover a similarity function from

an example that measures similarity between two ob-

jects. It is closely related to metric distance learning,

which ﬁnds a distance function between data. These

two areas are closely connected to transfer learning or

domain adaptation, where during the mapping of indi-

vidual features between domains, we search for suit-

able methods of comparing the similarity of these fea-

tures. We can then perform mapping based on these

similarities which is represented by different transfor-

mations. We distinguish between linear (e.g. Maha-

lanobis distance learning and linear similarity learn-

ing) and nonlinear (e.g. kernelization of linear meth-

ods) metric learning (Wang et al., 2014; Saenko et al.,

2010). The following survey on metric learning by

(Bellet et al., 2013; Kulis et al., 2013; Yang, 2006)

can serve for more details. Even though metric learn-

ing is a hot topic and is successfully used for problems

in computer vision and other ﬁelds (e.g. (G. Chechik

and Bengio., 2010; Kulis et al., 2009; Chopra et al.,

2005)) as far as we know it is rarely used in the ﬁeld of

transfer learning and domain adaptation. We can ﬁnd

one of few applications in the symmetric approach

by (Saenko et al., 2010) who present a method that

adapts object models to new imaging conditions by

supervised learning of transformations which mini-

mizes the effect of domain-induced changes in the

feature distribution.

This trend is similar to symmetric heterogeneous

learning. However if a model is trained on one do-

main and then tested on another domain, it often re-

sults in poor performance (Saenko et al., 2010). One

approach to this problem can be the generalization of

the metric learning problem (Kulis et al., 2011). The

idea is to learn a transformation A that maps the data

from one domain to the other, thus leading to the in-

ner product. This approach can be applied even when

the dimensionalities of the two domains are different

(Kulis et al., 2013).

(Chopra et al., 2005) propose a convolutional net-

work for mapping data from source to target space.

This method produces a non-linear mapping that can

map any input vector of features to its corresponding

version in lower dimension. It is also important that

meta-features are learned from data and do not stem

from prior knowledge about the task.

(Wang et al., 2014) propose a novel metric al-

gorithm to transfer knowledge from source to target

domain in metric settings called Cross-Domain Met-

ric Learning (CDML). This method consists of three

steps: 1) minimizing the distance between different

distributions, 2) constructing two Gaussian distribu-

tions, one based on Mahalanobis distance to be learnt,

second based on the information geometry (Wang

and Jin, 2009) of target domain data, 3) constructing

two more Gaussian distributions, one based on Maha-

lanobis distance again, the second one based on the

labels and the geometry of source domain data. The

results of these steps are combined into the uniﬁed

loss function of CDML and by this combination the

discriminating power gained from the labeled source

domain data to the unlabeled target domain data is

transferred.

Another usage of metric learning can be in un-

supervised domain adaptation, where labeled source

data and unlabeled target data for learning are avail-

able. The aim is to unify source and target distribu-

tions. The solution can be the usage of a nonpara-

metric way of measuring the distribution difference

between the source and target samples called Maxi-

mum Mean Discrepancy (MMD)(Bellet et al., 2013).

This is used by (Geng et al., 2011) in a domain adap-

tation metric learning (DAML) algorithm. Further we

encounter a transfer metric learning (TML) approach

by (Zhang and Yeung, 2010), where the metric and

the task covariances between the source and target

tasks are learnt under a uniﬁed convex formulation.

Their work is based on multi-task metric learning with

transfer learning settings.

4 CONCLUSION AND

CHALLENGES

The majority of heterogeneous transfer learning ap-

proaches transfer source and target features to com-

mon feature space (see Section 2.1). The minority

of works concern themselves with the direct mapping

of source features to target features (see Section 2.3),

which is signiﬁcantly more demanding because of the

necessity to search for a suitable mapping between

disparate but related spaces. The solutions are lim-

ited if we are dealing with labeled or unlabeled data.

Also a lot of work is done in computer vision and in

text classiﬁcation, but there are a lot of other domains,

e.g. medical data or student performance data, where

transfer learning could be applied. There are a lot of

manual mapping strategies, but the problem remains

in their automatization together with ﬁnding the opti-

mal mapping. Optimal mapping means that the map-

Asymmetric Heterogeneous Transfer Learning: A Survey

ping is feasible in both directions between source and

target feature spaces. A lot of data is task or domain

speciﬁc and so the generalization of mapping trans-

formations poses a challenge. There are two ways of

automatic feature mapping: trying multiple mappings

or mapping by analogy. This is often computation-

ally very demanding. There are also some complica-

tions with the lack of overlap between feature spaces

and different dimensionality. We also have to con-

sider, whether there is any correspondence between

features. One of the remaining questions is the nega-

tive transfer within asymmetric heterogeneous trans-

fer learning and varying data. Also the adaptation of

metrics to varying data (e.g. lifelong learning, detec-

tion of concept drifts).

The main contribution of this paper is to provide

a summary of available up-to-date approaches and

methods in the area of heterogeneous transfer learn-

ing. We also aim to emphasize some of the open chal-

lenges within this area. Our future work will consist

of ﬁnding suitable feature mappings between differ-

ent source and target spaces. We would like to use

these mapped features, more precisely the data, in

machine learning models which were learnt on data

not mapped and evaluate their relative performance.

This paper forms a base for future work in the ﬁeld

of asymmetric heterogeneous transfer learning using

methods of metric learning – this combination is not

very common as far as we know and thus it is one

of the main challenges which could bring an automa-

tized and generalized solution for asymmetric hetero-

geneous transfer learning problems.

ACKNOWLEDGEMENTS

This research has been supported by SGS grant No.

SGS17/210/OHK3/3T/18.

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