In this paper, we prove that the range space of an m × n matrix with rank r can be exactly recovered from a few coefficients with respect to general basis, though r and the number of corrupted samples are both as high as O(min{m, n}/ log3(m + n)).
We review the representative theories, algorithms and applications of the low rank subspace recovery models in data processing.
We discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations. Since R-PCA is the simplest, our discovery makes it the center of low-rank subspace recovery models.
We have investigated the exact recovery problem of R-PCA via Outlier Pursuit.