3 search results - page 1 / 1 » R1-PCA: rotational invariant L1-norm principal component ana... |
ICML
2006
IEEE
14 years 5 months ago
2006
IEEE
Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to the presence of outliers. We propose a rotational invariant L1-norm PCA (R1-PC...
PAMI
2008
13 years 4 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...
ICPR
2004
IEEE
14 years 6 months ago
2004
IEEE
Most face recognition approaches either assume constant lighting condition or standard facial expressions, thus cannot deal with both kinds of variations simultaneously. This prob...