187 search results - page 6 / 38 » Nonlinear Component Analysis as a Kernel Eigenvalue Problem |
ICPR
2008
IEEE
15 years 10 months ago
2008
IEEE
Speaker independent feature extraction is a critical problem in speech recognition. Oriented principal component analysis (OPCA) is a potential solution that can find a subspace r...
88
Voted
CVPR
2008
IEEE
15 years 11 months ago
2008
IEEE
Parameterized Appearance Models (PAMs) (e.g. eigentracking, active appearance models, morphable models) use Principal Component Analysis (PCA) to model the shape and appearance of...
85
Voted
ICML
2010
IEEE
14 years 9 months ago
2010
IEEE
We investigate the sparse eigenvalue problem which arises in various fields such as machine learning and statistics. Unlike standard approaches relying on approximation of the l0n...
86
Voted
IJCNN
2008
IEEE
15 years 3 months ago
2008
IEEE
— We propose a feature selection criterion based on kernel discriminant analysis (KDA) for an -class problem, which finds eigenvectors on which the projected class data are loca...
ICIP
2005
IEEE
15 years 11 months ago
2005
IEEE
Robustly tracking moving objects in video sequences is one of the key problems in computer vision. In this paper we introduce a computationally efficient nonlinear kernel learning...