A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910329185763328 |
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| author | Çakmak, Burak Lu, Yue M. Opper, Manfred |
| author_facet | Çakmak, Burak Lu, Yue M. Opper, Manfred |
| contents | Motivated by the recent application of approximate message passing (AMP) to the analysis of convex optimizations in multi-class classifications [Loureiro, et. al., 2021], we present a convergence analysis of AMP dynamics with non-separable multivariate nonlinearities. As an application, we present a complete (and independent) analysis of the motivated convex optimization problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08676 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification Çakmak, Burak Lu, Yue M. Opper, Manfred Machine Learning Information Theory Motivated by the recent application of approximate message passing (AMP) to the analysis of convex optimizations in multi-class classifications [Loureiro, et. al., 2021], we present a convergence analysis of AMP dynamics with non-separable multivariate nonlinearities. As an application, we present a complete (and independent) analysis of the motivated convex optimization problem. |
| title | A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification |
| topic | Machine Learning Information Theory |
| url | https://arxiv.org/abs/2402.08676 |