Co-transfer learning via joint transition probability graph based method

Kwok Po NG*, Qingyao Wu, Yunming Ye

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

20 Citations (Scopus)

Abstract

This paper studies a new machine learning strategy called co-transfer learning. Unlike many previous learning problems, we focus on how to use labeled data of different feature spaces to enhance the classification of different learning spaces simultaneously. For instance, we make use of both labeled images and labeled text data to help learn models for classifying image data and text data together. An important component of co-transfer learning is to build different relations to link different feature spaces, thus knowledge can be co-transferred across different spaces. Our idea is to model the problem as a joint transition probability graph. The transition probabilities can be constructed by using the intra-relationships based on affinity metric among instances and the inter-relationships based on co-occurrence information among instances from different spaces. The proposed 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 random walk with restart. The main contribution of this paper is to (i) propose a co-transfer learning (CT-Learn) framework that can perform learning simultaneously by co-transferring knowledge across different spaces; (ii) show the theoretical properties of the random walk for such joint transition probability graph so that the proposed learning model can be used effectively; (iii) develop an efficient algorithm to compute ranking scores and generate the possible labels for a given instance. Experimental results on benchmark data (image-text and English-Chinese-French classification data sets) have shown that the proposed algorithm is computationally efficient, and effective in learning across different spaces. In the comparison, we find that the classification performance of the CT-Learn algorithm is better than those of the other tested transfer learning algorithms.

Original languageEnglish
Title of host publicationProceedings of the ACM SIGKDD Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining 2012, CDKD 2012
Pages1-9
Number of pages9
DOIs
Publication statusPublished - 2012
Event1st ACM SIGKDD Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining 2012, CDKD 2012 - Beijing, China
Duration: 12 Aug 201216 Aug 2012

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference1st ACM SIGKDD Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining 2012, CDKD 2012
Country/TerritoryChina
CityBeijing
Period12/08/1216/08/12

Scopus Subject Areas

  • Software
  • Information Systems

User-Defined Keywords

  • Classification
  • Co-transfer learning
  • Coupled markov chain
  • Iterative methods
  • Joint probability graph
  • Labels ranking
  • Transfer learning

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