58 search results - page 3 / 12 » Principal Component Analysis with Contaminated Data: The Hig... |
KDD
2001
ACM
14 years 5 months ago
2001
ACM
Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the method preserves distances quite nicely; however,...
DCC
2009
IEEE
14 years 5 months ago
2009
IEEE
An analysis is presented that extends existing Rayleigh-Ritz theory to the special case of highly eccentric distributions. Specifically, a bound on the angle between the first Rit...
ICCV
2001
IEEE
14 years 7 months ago
2001
IEEE
Principal Component Analysis (PCA) has been widely used for the representation of shape, appearance, and motion. One drawback of typical PCA methods is that they are least squares...
ICML
2004
IEEE
14 years 6 months ago
2004
IEEE
Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a ...
IJCV
2008
13 years 5 months ago
2008
For software and more illustrations: http://www.psi.utoronto.ca/anitha/fastTCA.htm Dimensionality reduction techniques such as principal component analysis and factor analysis are...