Cotransfer learning using coupled markov chains with restart

Qingyao Wu*, Kwok Po NG, Yunming Ye

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number6908908
Pages (from-to)26-33
Number of pages8
JournalIEEE Intelligent Systems
Volume29
Issue number4
DOIs
Publication statusPublished - 1 Jul 2014

Scopus Subject Areas

  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

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

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