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121
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ICML
2006
IEEE
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Machine Learning
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ICML 2006
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R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
16 years 3 months ago
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ranger.uta.edu
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...
Chris H. Q. Ding, Ding Zhou, Xiaofeng He, Hongyuan...
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