تقرير
Deep Unrolling for Nonconvex Robust Principal Component Analysis
| العنوان: | Deep Unrolling for Nonconvex Robust Principal Component Analysis |
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| المؤلفون: | Tan, Elizabeth Z. C., Chaux, Caroline, Soubies, Emmanuel, Tan, Vincent Y. F. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning |
| الوصف: | 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. Comment: 7 pages, 3 figures; Accepted to the 2023 IEEE International Workshop on Machine Learning for Signal Processing |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2307.05893 |
| رقم الأكسشن: | edsarx.2307.05893 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |