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Efficient Robust Principal Component Analysis via Block Krylov Iteration and CUR Decomposition
Specifically, the Krylov iteration method is employed to approximate the eigenvalue decomposition in the rank estimation, which requires O(ndrq + n(rq)^2) for an (nxd) input matrix, in which q is a parameter with a small value, r is the target rank.
Adaptive Rank Estimate in Robust Principal Component Analysis
In this paper, an adaptive rank estimate based RPCA (ARE-RPCA) is proposed, which adaptively assigns weights on different singular values via rank estimation.
Illumination Normalization via Merging Locally Enhanced Textures for Robust Face Recognition
Finally, the local contrast equalization (LCE) is performed on the fused face image to reduce the influence caused by over or under saturated pixel values in highlight or dark regions.
