$\ell_0$ factor analysis
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909388148572160 |
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| author | Wang, Linyang Liu, Wanquan Zhu, Bin |
| author_facet | Wang, Linyang Liu, Wanquan Zhu, Bin |
| contents | Factor Analysis is about finding a low-rank plus sparse additive decomposition from a noisy estimate of the signal covariance matrix. In order to get such a decomposition, we formulate an optimization problem using the nuclear norm for the low-rank component, the $\ell_0$ norm for the sparse component, and the Kullback-Leibler divergence to control the residual in the sample covariance matrix. An alternating minimization algorithm is designed for the solution of the optimization problem. The effectiveness of the algorithm is verified via simulations on synthetic and real datasets. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_08468 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | $\ell_0$ factor analysis Wang, Linyang Liu, Wanquan Zhu, Bin Optimization and Control Machine Learning Factor Analysis is about finding a low-rank plus sparse additive decomposition from a noisy estimate of the signal covariance matrix. In order to get such a decomposition, we formulate an optimization problem using the nuclear norm for the low-rank component, the $\ell_0$ norm for the sparse component, and the Kullback-Leibler divergence to control the residual in the sample covariance matrix. An alternating minimization algorithm is designed for the solution of the optimization problem. The effectiveness of the algorithm is verified via simulations on synthetic and real datasets. |
| title | $\ell_0$ factor analysis |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2411.08468 |