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Solving Principal Component Pursuit in Linear Time via $l_1$ Filtering
In this paper, we propose a novel algorithm, called $l_1$ filtering, for \emph{exactly} solving PCP with an $O(r^2(m+n))$ complexity, where $m\times n$ is the size of data matrix and $r$ is the rank of the matrix to recover, which is supposed to be much smaller than $m$ and $n$.
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms
Recent years have witnessed the popularity of using rank minimization as a regularizer for various signal processing and machine learning problems.
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