Deep Unrolling for Nonconvex Robust Principal Component Analysis
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Abstract (arXiv)
We design algorithms for Robust Principal Component Analysis (RPCA) which consists in decomposing a matrix into the sum of a low rank matrix and a sparse matrix. We propose a deep unrolled algorithm based on an accelerated alternating projection algorithm which aims to solve RPCA in its nonconvex form. The proposed procedure combines benefits of deep neural networks and the interpretability of the original algorithm and it automatically learns hyperparameters. We demonstrate the unrolled algorithm's effectiveness on synthetic datasets and also on a face modeling problem, where it leads to both better numerical and visual performances.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 12 Jul 2023.
Builds on
10 of 24 references.
References · 10
- 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 411
- 2018Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasoundcited by 43
- 2014Non-convex Robust PCAcited by 41
- 2019Deep learning in ultrasound imagingcited by 38
- 2021Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detectioncited by 34
- 2017Accelerated Alternating Projections for Robust Principal Component Analysiscited by 25
- 2020Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimationcited by 16
- 2020A Deep-Unfolded Reference-Based RPCA Network For Video Foreground-Background Separationcited by 6
- 2022Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiographycited by 5
- 2021Multimodal Unrolled Robust PCA for Background Foreground Separationcited by 4
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.