Together with the analytic solutions to p-norm minimization with two specific values of p, i.e., p = 1/2 and p = 2/3, we propose two novel bilinear factor matrix norm minimization models for robust principal component analysis.

We survey the applications of RPCA in computer vision

We propose a novel algorithm, called ℓ1 filtering, for exactly solving PCP with an complexity, where m×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.

Powered by the Xia Li @ ZERO Lab, Peking University.