Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Zhai, Wei and Zhang, Fanlong (2024) Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors. Journal of Computer and Communications, 12 (04). pp. 1-13. ISSN 2327-5219

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Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.

Item Type: Article
Subjects: Eprints STM archive > Computer Science
Depositing User: Unnamed user with email admin@eprints.stmarchive
Date Deposited: 06 Apr 2024 13:03
Last Modified: 06 Apr 2024 13:03

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