Tuning-Free Online Robust Principal Component Analysis through Implicit Regularization
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912536499060736 |
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| author | Jayalal, Lakshmi Muthukrishnan, Gokularam Kalyani, Sheetal |
| author_facet | Jayalal, Lakshmi Muthukrishnan, Gokularam Kalyani, Sheetal |
| contents | The performance of the standard Online Robust Principal Component Analysis (OR-PCA) technique depends on the optimum tuning of the explicit regularizers and this tuning is dataset sensitive. We aim to remove the dependency on these tuning parameters by using implicit regularization. We propose to use the implicit regularization effect of various modified gradient descents to make OR-PCA tuning free. Our method incorporates three different versions of modified gradient descent that separately but naturally encourage sparsity and low-rank structures in the data. The proposed method performs comparable or better than the tuned OR-PCA for both simulated and real-world datasets. Tuning-free ORPCA makes it more scalable for large datasets since we do not require dataset-dependent parameter tuning. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_07275 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tuning-Free Online Robust Principal Component Analysis through Implicit Regularization Jayalal, Lakshmi Muthukrishnan, Gokularam Kalyani, Sheetal Machine Learning Computer Vision and Pattern Recognition The performance of the standard Online Robust Principal Component Analysis (OR-PCA) technique depends on the optimum tuning of the explicit regularizers and this tuning is dataset sensitive. We aim to remove the dependency on these tuning parameters by using implicit regularization. We propose to use the implicit regularization effect of various modified gradient descents to make OR-PCA tuning free. Our method incorporates three different versions of modified gradient descent that separately but naturally encourage sparsity and low-rank structures in the data. The proposed method performs comparable or better than the tuned OR-PCA for both simulated and real-world datasets. Tuning-free ORPCA makes it more scalable for large datasets since we do not require dataset-dependent parameter tuning. |
| title | Tuning-Free Online Robust Principal Component Analysis through Implicit Regularization |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.07275 |