<ul><li>Researchers propose a novel Robust Principal Component Analysis (RPCA) model for decomposing data into low-rank and sparse components.</li><li>The model integrates adaptive weighted least squares (AWLS) and low-rank matrix factorization (LRMF).</li><li>It employs a self-attention-inspired mechanism to dynamically adjust and emphasize significant components.</li><li>The proposed method outperforms existing non-convex regularization approaches in terms of performance, stability, accuracy, and robustness.</li></ul>

Robust PCA Based on Adaptive Weighted Least Squares and Low-Rank Matrix Factorization

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