Solving Principal Component Pursuit in Linear Time via $l_1$ Filtering

26 Aug 2011Risheng LiuZhouchen LinSiming WeiZhixun Su

In the past decades, exactly recovering the intrinsic data structure from corrupted observations, which is known as robust principal component analysis (RPCA), has attracted tremendous interests and found many applications in computer vision. Recently, this problem has been formulated as recovering a low-rank component and a sparse component from the observed data matrix... (read more)

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