Authors:
Jorge A. Vanegas
;
Viviana Beltran
and
Fabio A. González
Affiliation:
Universidad Nacional de Colombia, Colombia
Keyword(s):
Machine Learning, Multi-label Annotation, Semantic Embedding, Online Learning.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Matrix Factorization
;
Multi-Instance Learning
;
On-Line Learning
;
Pattern Recognition
;
Stochastic Methods
;
Theory and Methods
Abstract:
This paper presents a matrix factorization algorithm for multi-label annotation. The multi-label annotation
problem arises in situations such as object recognition in images where we want to automatically find the
objects present in a given image. The solution consists in learning a classification model able to assign one
or many labels to a particular sample. The method presented in this paper learns a mapping between the
features of the input sample and the labels, which is later used to predict labels for unannotated instances.
The mapping between the feature representation and the labels is found by learning a common semantic
representation using matrix factorization. An important characteristic of the proposed algorithm is its online
formulation based on stochastic gradient descent which can scale to deal with large datasets. According to the
experimental evaluation, which compares the method with state-of-the-art space embedding algorithms, the
proposed method presents a compet
itive performance improving, in some cases, previously reported results.
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