Block Sparse Bayesian Learning: A Diversified Scheme
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929567837454336 |
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| author | Zhang, Yanhao Zhu, Zhihan Xia, Yong |
| author_facet | Zhang, Yanhao Zhu, Zhihan Xia, Yong |
| contents | This paper introduces a novel prior called Diversified Block Sparse Prior to characterize the widespread block sparsity phenomenon in real-world data. By allowing diversification on intra-block variance and inter-block correlation matrices, we effectively address the sensitivity issue of existing block sparse learning methods to pre-defined block information, which enables adaptive block estimation while mitigating the risk of overfitting. Based on this, a diversified block sparse Bayesian learning method (DivSBL) is proposed, utilizing EM algorithm and dual ascent method for hyperparameter estimation. Moreover, we establish the global and local optimality theory of our model. Experiments validate the advantages of DivSBL over existing algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04646 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Block Sparse Bayesian Learning: A Diversified Scheme Zhang, Yanhao Zhu, Zhihan Xia, Yong Machine Learning Optimization and Control This paper introduces a novel prior called Diversified Block Sparse Prior to characterize the widespread block sparsity phenomenon in real-world data. By allowing diversification on intra-block variance and inter-block correlation matrices, we effectively address the sensitivity issue of existing block sparse learning methods to pre-defined block information, which enables adaptive block estimation while mitigating the risk of overfitting. Based on this, a diversified block sparse Bayesian learning method (DivSBL) is proposed, utilizing EM algorithm and dual ascent method for hyperparameter estimation. Moreover, we establish the global and local optimality theory of our model. Experiments validate the advantages of DivSBL over existing algorithms. |
| title | Block Sparse Bayesian Learning: A Diversified Scheme |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2402.04646 |