Variable Clustering via Distributionally Robust Nodewise Regression
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866913177095110656 |
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| author | Wang, Kaizheng Xu, Xiao Zhou, Xun Yu |
| author_facet | Wang, Kaizheng Xu, Xiao Zhou, Xun Yu |
| contents | We study a multi-factor block model for variable clustering and connect it to regularized subspace clustering through a distributionally robust version of nodewise regression. To solve the latter problem, we derive a convex relaxation, provide a data-driven approach for selecting the size of the robust region, and develop an ADMM algorithm for efficient implementation. We validate our method in extensive numerical studies and demonstrate its superior performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_07944 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Variable Clustering via Distributionally Robust Nodewise Regression Wang, Kaizheng Xu, Xiao Zhou, Xun Yu Machine Learning Optimization and Control Computational Finance Portfolio Management Statistical Finance We study a multi-factor block model for variable clustering and connect it to regularized subspace clustering through a distributionally robust version of nodewise regression. To solve the latter problem, we derive a convex relaxation, provide a data-driven approach for selecting the size of the robust region, and develop an ADMM algorithm for efficient implementation. We validate our method in extensive numerical studies and demonstrate its superior performance. |
| title | Variable Clustering via Distributionally Robust Nodewise Regression |
| topic | Machine Learning Optimization and Control Computational Finance Portfolio Management Statistical Finance |
| url | https://arxiv.org/abs/2212.07944 |