Robust PCA and subspace tracking from incomplete observations using L0-surrogates

2 Oct 2012Clemens HageMartin Kleinsteuber

Many applications in data analysis rely on the decomposition of a data matrix into a low-rank and a sparse component. Existing methods that tackle this task use the nuclear norm and L1-cost functions as convex relaxations of the rank constraint and the sparsity measure, respectively, or employ thresholding techniques... (read more)

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