Block Sparse Bayesian Learning: A Diversified Scheme

Fuente: arXiv

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Dettagli Bibliografici
Autori principali:	Zhang, Yanhao, Zhu, Zhihan, Xia, Yong
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Optimization and Control
Accesso online:	Acceder al recurso
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_version_	1866929567837454336
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

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