910 search results - page 11 / 182 » Progressive Principal Component Analysis |
NIPS
2008
13 years 9 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...
PAMI
2008
13 years 7 months ago
2008
In data-analysis problems with a large number of dimension, principal component analysis based on L2-norm (L2PCA) is one of the most popular methods, but L2-PCA is sensitive to out...
COLT
2010
Springer
13 years 5 months ago
2010
Springer
We consider the dimensionality-reduction problem (finding a subspace approximation of observed data) for contaminated data in the high dimensional regime, where the number of obse...
ECML
2004
Springer
14 years 28 days ago
2004
Springer
This work presents a novel procedure for computing (1) distances between nodes of a weighted, undirected, graph, called the Euclidean Commute Time Distance (ECTD), and (2) a subspa...
ICPR
2000
IEEE
14 years 8 months ago
2000
IEEE
This paper describes a new method to recover the sign of the local Gaussian curvature at each point on the visible surface of a 3-D object. Multiple (p > 3) shaded images are a...