Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Cotransfer learning using coupled Markov chains with restart

Language

English

Abstract

© 2001-2011 IEEE. This article studies cotransfer learning, a machine learning strategy that uses labeled data to enhance the classification of different learning spaces simultaneously. The authors model the problem as a coupled Markov chain with restart. The transition probabilities in the coupled Markov chain can be constructed using the intrarelationships based on the affinity metric among instances in the same space, and the interrelationships based on co-occurrence information among instances from different spaces. The learning algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the coupled Markov chain with restart. Experimental results on benchmark data (multiclass image-text and English-Spanish-French classification datasets) have shown that the learning algorithm is computationally efficient, and effective in learning across different spaces.

Keywords

classification, cotransfer learning, coupled Markov chains, Intelligent systems, iterative methods, labels ranking, transfer learning

Publication Date

2014

Source Publication Title

IEEE Intelligent Systems

Volume

29

Issue

4

Start Page

26

End Page

33

Publisher

Institute of Electrical and Electronics Engineers

DOI

10.1109/MIS.2013.32

Link to Publisher's Edition

http://dx.doi.org/10.1109/MIS.2013.32

ISSN (print)

15411672

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