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ICCV
2001
IEEE
14 years 9 months ago
Robust Principal Component Analysis for Computer Vision
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...
Fernando De la Torre, Michael J. Black
GPB
2010
231views Solid Modeling» more  GPB 2010»
13 years 5 months ago
Mining Gene Expression Profiles: An Integrated Implementation of Kernel Principal Component Analysis and Singular Value Decompos
The detection of genes that show similar profiles under different experimental conditions is often an initial step in inferring the biological significance of such genes. Visualiz...
Ferran Reverter, Esteban Vegas, Pedro Sánch...
AMCS
2008
146views Mathematics» more  AMCS 2008»
13 years 8 months ago
Fault Detection and Isolation with Robust Principal Component Analysis
Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA which is based on the estimation of the sample mean and covariance...
Yvon Tharrault, Gilles Mourot, José Ragot, ...
KDD
2006
ACM
115views Data Mining» more  KDD 2006»
14 years 8 months ago
Supervised probabilistic principal component analysis
Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When label...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...