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Video-Surveillance-via-Robust-PCA

The code performs matrix factorization on video data, separating it into low-rank (L) and sparse (S) components using an iterative algorithm. It updates L and S matrices by minimizing the difference between the input video data (X) and their sum, incorporating a regularization parameter (lambda) and adjusting a Lagrange multiplier (ro).

The code tracks the convergence of the factorization process, displaying residual error values in a log-log plot to assess the quality of the factorization over iterations.

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The project is a straightforward video processing system, designed to perform singular value decomposition (SVD) on video images.

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