Multiclass probabilistic kernel discriminant analysis

Zheng Zhao, Liang Sun, Shipeng Yu, Huan Liu, Jieping Ye

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

8 Scopus citations

Abstract

Kernel discriminant analysis (KDA) is an effective approach for supervised nonlinear dimensionality reduction. Probabilistic models can be used with KDA to improve its robustness. However, the state of the art of such models could only handle binary class problems, which confines their application in many real world problems. To overcome this limitation, we propose a novel nonparametric probabilistic model based on Gaussian Process for KDA to handle multiclass problems. The model provides a novel Bayesian interpretation for KDA, which allows its parameters to be automatically tuned through the optimization of the marginal log-likelihood of the data. Empirical study demonstrates the efficacy of the proposed model.

Original languageEnglish (US)
Title of host publicationIJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence
Pages1363-1368
Number of pages6
ISBN (Print)9781577354260
StatePublished - Jan 1 2009
Event21st International Joint Conference on Artificial Intelligence, IJCAI 2009 - Pasadena, United States
Duration: Jul 11 2009Jul 16 2009

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference21st International Joint Conference on Artificial Intelligence, IJCAI 2009
CountryUnited States
CityPasadena
Period7/11/097/16/09

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Zhao, Z., Sun, L., Yu, S., Liu, H., & Ye, J. (2009). Multiclass probabilistic kernel discriminant analysis. In IJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence (pp. 1363-1368). (IJCAI International Joint Conference on Artificial Intelligence). International Joint Conferences on Artificial Intelligence.