Robust PCA via non-convex half-quadratic regularization

Wang, Zhi-Yong; Li, Xiao Peng; So, Hing Cheung; Liu, Zhaofeng

doi:10.1016/j.sigpro.2022.108816

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…The proposed algorithm is compared with four state-of-the-art RPCA algorithms, namely, RPCA-HQF [9], accelerated alternating projections (Ac-cAltProj) [23], TNNR-APGL [18] and RCPA-CUR [20]. In our experiments, the parameters of algorithms are tuned to achieve the best performance.…”

Section: Methodsmentioning

confidence: 99%

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Zhai,

Zhang

2024

JCC

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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.

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Section: Methodsmentioning

confidence: 99%

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Zhai,

Zhang

2024

JCC

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“…Synthetic Datasets For synthetic datasets, we compare LERE against SOTA RPCA-based methods categorized into two groups: The first category is based on the estimated rank, including ARE-RPCA (Xu et al 2021a) and ADW-RPCA (Xu et al 2021b). The second group is grounded on prior rank, featuring AccAltProj (Cai, Cai, and Wei 2019), ScaledGD (Tong, Ma, and Chi 2021), RPCA_HQF (Wang et al 2023), and LRPCA (Cai, Liu, and Yin 2021).…”

Section: Comparison Of Various Rpcamentioning

confidence: 99%

LERE: Learning-Based Low-Rank Matrix Recovery with Rank Estimation

Xu,

Zhang,

et al. 2024

AAAI

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A fundamental task in the realms of computer vision, Low-Rank Matrix Recovery (LRMR) focuses on the inherent low-rank structure precise recovery from incomplete data and/or corrupted measurements given that the rank is a known prior or accurately estimated. However, it remains challenging for existing rank estimation methods to accurately estimate the rank of an ill-conditioned matrix. Also, existing LRMR optimization methods are heavily dependent on the chosen parameters, and are therefore difficult to adapt to different situations. Addressing these issues, A novel LEarning-based low-rank matrix recovery with Rank Estimation (LERE) is proposed. More specifically, considering the characteristics of the Gerschgorin disk's center and radius, a new heuristic decision rule in the Gerschgorin Disk Theorem is significantly enhanced and the low-rank boundary can be exactly located, which leads to a marked improvement in the accuracy of rank estimation. According to the estimated rank, we select row and column sub-matrices from the observation matrix by uniformly random sampling. A 17-iteration feedforward-recurrent-mixed neural network is then adapted to learn the parameters in the sub-matrix recovery processing. Finally, by the correlation of the row sub-matrix and column sub-matrix, LERE successfully recovers the underlying low-rank matrix. Overall, LERE is more efficient and robust than existing LRMR methods. Experimental results demonstrate that LERE surpasses state-of-the-art (SOTA) methods. The code for this work is accessible at https://github.com/zhengqinxu/LERE.

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Robust tensor ring-based graph completion for incomplete multi-view clustering

Xing,

Chen,

et al. 2024

Information Fusion

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Robust PCA via non-convex half-quadratic regularization

Cited by 5 publications

References 29 publications

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

LERE: Learning-Based Low-Rank Matrix Recovery with Rank Estimation

Robust tensor ring-based graph completion for incomplete multi-view clustering

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