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ICDM
2009
IEEE
174views Data Mining» more  ICDM 2009»
14 years 4 months ago
Non-sparse Multiple Kernel Learning for Fisher Discriminant Analysis
—We consider the problem of learning a linear combination of pre-specified kernel matrices in the Fisher discriminant analysis setting. Existing methods for such a task impose a...
Fei Yan, Josef Kittler, Krystian Mikolajczyk, Muha...
SPEECH
1998
171views more  SPEECH 1998»
13 years 9 months ago
Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition
We present the theory for heteroscedastic discriminant analysis (HDA), a model-based generalization of linear discriminant analysis (LDA) derived in the maximum-likelihood framewo...
Nagendra Kumar, Andreas G. Andreou
BMVC
2001
14 years 9 days ago
Recognising Trajectories of Facial Identities Using Kernel Discriminant Analysis
We present a comprehensive approach to address three challenging problems in face recognition: modelling faces across multi-views, extracting the non-linear discriminating feature...
Yongmin Li, Shaogang Gong, Heather M. Liddell
PR
2007
145views more  PR 2007»
13 years 9 months ago
Face recognition using a kernel fractional-step discriminant analysis algorithm
Feature extraction is among the most important problems in face recognition systems. In this paper, we propose an enhanced kernel discriminant analysis (KDA) algorithm called kern...
Guang Dai, Dit-Yan Yeung, Yuntao Qian
NIPS
2007
13 years 11 months ago
Testing for Homogeneity with Kernel Fisher Discriminant Analysis
We propose to investigate test statistics for testing homogeneity based on kernel Fisher discriminant analysis. Asymptotic null distributions under null hypothesis are derived, an...
Zaïd Harchaoui, Francis Bach, Eric Moulines