JMLR
2010
13 years 6 months ago
2010
This study explores the feasibility of estimating the Body Condition Score (BCS) of cows from digital images by employing statistical shape analysis and regression machines. The s...
IGARSS
2009
13 years 9 months ago
2009
Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remotesensing data. Features extracted using KPCA are used to construct the Ex...
PAMI
2010
13 years 9 months ago
2010
—A new formulation for multiway spectral clustering is proposed. This method corresponds to a weighted kernel principal component analysis (PCA) approach based on primal-dual lea...
IJON
2007
13 years 11 months ago
2007
We present in this paper a novel approach for shape description based on kernel principal component analysis (KPCA). The strength of this method resides in the similarity (rotatio...
NIPS
2003
14 years 24 days ago
2003
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solu...
NIPS
2001
14 years 24 days ago
2001
We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding...
NIPS
2004
14 years 24 days ago
2004
This paper investigates the effect of Kernel Principal Component Analysis (KPCA) within the classification framework, essentially the regularization properties of this dimensional...
ESANN
2003
14 years 24 days ago
2003
Abstract. We focus on covariance criteria for finding a suitable subspace for regression in a reproducing kernel Hilbert space: kernel principal component analysis, kernel partial...
ACL
2004
14 years 25 days ago
2004
We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy superior to the best pu...
NIPS
2008
14 years 26 days 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...