Hybrid Vector Message Passing for Generalized Bilinear Factorization
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911750339690496 |
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| author | Jiang, Hao Yuan, Xiaojun Guo, Qinghua |
| author_facet | Jiang, Hao Yuan, Xiaojun Guo, Qinghua |
| contents | In this paper, we propose a new message passing algorithm that utilizes hybrid vector message passing (HVMP) to solve the generalized bilinear factorization (GBF) problem. The proposed GBF-HVMP algorithm integrates expectation propagation (EP) and variational message passing (VMP) via variational free energy minimization, yielding tractable Gaussian messages. Furthermore, GBF-HVMP enables vector/matrix variables rather than scalar ones in message passing, resulting in a loop-free Bayesian network that improves convergence. Numerical results show that GBF-HVMP significantly outperforms state-of-the-art methods in terms of NMSE performance and computational complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03626 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hybrid Vector Message Passing for Generalized Bilinear Factorization Jiang, Hao Yuan, Xiaojun Guo, Qinghua Signal Processing In this paper, we propose a new message passing algorithm that utilizes hybrid vector message passing (HVMP) to solve the generalized bilinear factorization (GBF) problem. The proposed GBF-HVMP algorithm integrates expectation propagation (EP) and variational message passing (VMP) via variational free energy minimization, yielding tractable Gaussian messages. Furthermore, GBF-HVMP enables vector/matrix variables rather than scalar ones in message passing, resulting in a loop-free Bayesian network that improves convergence. Numerical results show that GBF-HVMP significantly outperforms state-of-the-art methods in terms of NMSE performance and computational complexity. |
| title | Hybrid Vector Message Passing for Generalized Bilinear Factorization |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2401.03626 |