Department of Mathematics
Cotransfer learning using coupled Markov chains with restart
© 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.
classification, cotransfer learning, coupled Markov chains, Intelligent systems, iterative methods, labels ranking, transfer learning
Source Publication Title
IEEE Intelligent Systems
Institute of Electrical and Electronics Engineers
Link to Publisher's Edition
Wu, Qingyao, Michael K. Ng, and Yunming Ye. "Cotransfer learning using coupled Markov chains with restart." IEEE Intelligent Systems 29.4 (2014): 26-33.