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Co-Transfer Learning Using Coupled Markov Chains with Restart
PrePrint
ISSN: 1541-1672
Michael Ng, Hong Kong Baptist University , Hong Kong
Qingyao Wu, Harbin Institute of Technology, Shenzhen
Yunming Ye, Harbin Institute of Technology, Shenzhen
This paper studies a machine learning strategy called co-transfer learning. Unlike many previous transfer learning problems, we focus on how to use labeled data of different feature spaces to enhance the classification of different learning spaces simultaneously. Our idea is to model the problem as a coupled Markov chain with restart. The transition probabilities in the coupled Markov chain can be constructed by using the intra-relationships based on affinity metric among instances in the same space, and the inter-relationships 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 (multi-class image-text and English-Spanish-French classification data sets) have shown that the learning algorithm is computationally efficient, and effective in learning across different spaces.
Citation:
Michael Ng, Qingyao Wu, Yunming Ye, "Co-Transfer Learning Using Coupled Markov Chains with Restart," IEEE Intelligent Systems, 08 March 2013. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/MIS.2013.32>
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