773 search results - page 24 / 155 » Kernel Change-point Analysis |
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Voted
ICDM
2009
IEEE
15 years 4 months ago
2009
IEEE
—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...
ECCV
2006
Springer
15 years 11 months ago
2006
Springer
Many linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD) methods are based on the restrictive assumption that the data are homoscedastic. In this paper...
TKDE
2008
14 years 9 months ago
2008
Linear and kernel discriminant analyses are popular approaches for supervised dimensionality reduction. Uncorrelated and regularized discriminant analyses have been proposed to ove...
JMLR
2002
14 years 9 months ago
2002
We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On th...
NIPS
2008
14 years 11 months ago
2008
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...