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Authors: Magda Friedjungová and Marcel Jiřina

Affiliation: Czech Technical University in Prague, Czech Republic

ISBN: 978-989-758-255-4

Keyword(s): Asymmetric Heterogeneous Transfer Learning, Different Feature Space, Domain Adaptation, Survey, Data Mining, Metric Learning.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Collaboration and e-Services ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; e-Business ; Enterprise Information Systems ; Health Information Systems ; Information Integration ; Information Retrieval ; Integration/Interoperability ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Pattern Recognition ; Predictive Modeling ; Sensor Networks ; Signal Processing ; Soft Computing ; Software Engineering ; Symbolic Systems

Abstract: One of the main prerequisites in most machine learning and data mining tasks is that all available data originates 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 combination 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. (More)

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Paper citation in several formats:
Friedjungová, M. and Jiřina, M. (2017). Asymmetric Heterogeneous Transfer Learning: A Survey.In Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-255-4, pages 17-27. DOI: 10.5220/0006396700170027

@conference{data17,
author={Magda Friedjungová. and Marcel Ji\v{r}ina.},
title={Asymmetric Heterogeneous Transfer Learning: A Survey},
booktitle={Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2017},
pages={17-27},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006396700170027},
isbn={978-989-758-255-4},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Asymmetric Heterogeneous Transfer Learning: A Survey
SN - 978-989-758-255-4
AU - Friedjungová, M.
AU - Jiřina, M.
PY - 2017
SP - 17
EP - 27
DO - 10.5220/0006396700170027

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