52 search results - page 6 / 11 » Nonlinear kernel density principal component analysis with a... |
ACCV
2007
Springer
14 years 27 days ago
2007
Springer
A novel kernel discriminant transformation (KDT) algorithm based on the concept of canonical differences is presented for automatic face recognition applications. For each individu...
ICCV
2007
IEEE
14 years 1 months ago
2007
IEEE
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
ECCV
2010
Springer
13 years 11 months ago
2010
Springer
Manifolds are widely used to model non-linearity arising in a range of computer vision applications. This paper treats statistics on manifolds and the loss of accuracy occurring wh...
PAMI
2002
13 years 6 months ago
2002
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
ICPR
2008
IEEE
14 years 8 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...