Subspace Recovery

Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis

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)).

A Review on Low-Rank Models in Signal and Data Analysis

We review the representative theories, algorithms and applications of the low rank subspace recovery models in data processing.

Relations among Some Low Rank Subspace Recovery Models

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.

Exact Recoverability of Robust PCA via Outlier Pursuit with Tight Recovery Bounds

We have investigated the exact recovery problem of R-PCA via Outlier Pursuit.