On multiple kernel learning with multiple labels

Lei Tang, Jianhui Chen, Jieping Ye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations

Abstract

For classification with multiple labels, a common approach is to learn a classifier for each label. With a kernel-based classifier, there are two options to set up kernels: select a specific kernel for each label or the same kernel for all labels. In this work, we present a unified framework for multi-label multiple kernel learning, in which the above two approaches can be considered as two extreme cases. Moreover, our framework allows the kernels shared partially among multiple labels, enabling flexible degrees of label commonality. We systematically study how the sharing of kernels among multiple labels affects the performance based on extensive experiments on various benchmark data including images and microarray data. Interesting findings concerning efficacy and efficiency are reported.

Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
Pages1255-1260
Number of pages6
Publication statusPublished - 2009
Event21st International Joint Conference on Artificial Intelligence, IJCAI-09 - Pasadena, CA, United States
Duration: Jul 11 2009Jul 17 2009

Other

Other21st International Joint Conference on Artificial Intelligence, IJCAI-09
CountryUnited States
CityPasadena, CA
Period7/11/097/17/09

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Tang, L., Chen, J., & Ye, J. (2009). On multiple kernel learning with multiple labels. In IJCAI International Joint Conference on Artificial Intelligence (pp. 1255-1260)