Learning kernels from labels with ideal regularization

Binbin Pan, Jianhuang Lai, Lixin Shen

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

Abstract

In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. We first present the definition of extended ideal kernel for both labeled and unlabeled data of multiple classes. Based on this extended ideal kernel, we propose an ideal regularization which is a linear function of the kernel matrix to be learned. The ideal regularization allows us to develop effective algorithms to exploit labels. Two applications of the ideal regularization are considered. Empirical results show the ideal regularization exploits the labels effectively.

Original languageEnglish (US)
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages505-508
Number of pages4
StatePublished - Dec 1 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: Nov 11 2012Nov 15 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period11/11/1211/15/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Pan, B., Lai, J., & Shen, L. (2012). Learning kernels from labels with ideal regularization. In ICPR 2012 - 21st International Conference on Pattern Recognition (pp. 505-508). [6460182] (Proceedings - International Conference on Pattern Recognition).