Multiclass Probabilistic Kernel Discriminant Analysis
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1363-1367.
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 loglikelihood of the data. Empirical study demonstrates the efficacy of the proposed model.Zheng Zhao, Liang Sun, Shipeng Yu, Huan Liu, Jieping Ye
@inproceedings{ijcai2009p229,
title = {Multiclass Probabilistic Kernel Discriminant Analysis},
author = {Zhao, Zheng and Sun, Liang and Yu, Shipeng and Liu, Huan and Ye, Jieping},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1363--1367},
year = {2009},
url = {https://ijcai.org/proceedings/2009/229},
}
TY - CPAPER TI - Multiclass Probabilistic Kernel Discriminant Analysis AU - Zhao, Zheng AU - Sun, Liang AU - Yu, Shipeng AU - Liu, Huan AU - Ye, Jieping T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1363 EP - 1367 UR - https://ijcai.org/proceedings/2009/229 ER -Copied
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