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Robust Principal Component Analysis on Graphs.

ICCV, no. 1 (2015): 2812-2820

Cited: 115|Views40
EI

Abstract

Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA solves the first issue with a sparse penalty term. The second issue can be handled with the matrix facto...More

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