Article citationsMore>>

Chen, Y., Wang, S. and Zhou, Y. (2018) Tensor Nuclear Norm-Based Low-Rank Approximation with Total Variation Regularization. IEEE Journal of Selected Topics in Signal Processing, 12, 1364-1377.
https://doi.org/10.1109/JSTSP.2018.2873148

has been cited by the following article:

• TITLE: Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

  AUTHORS: Wei Zhai, Fanlong Zhang

  KEYWORDS: Robust Principal Component Analysis, Sparse Matrix, Low-Rank Matrix, Hyperspectral Image

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.4, April 2, 2024

  ABSTRACT: 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.