Robust Principal Component Analysis

Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications

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.

On the Applications of Robust PCA in Image and Video Processing

We survey the applications of RPCA in computer vision

Linear time Principal Component Pursuit and its extensions using l1 Filtering

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.