Block Sparse Bayesian Learning: A Diversified Scheme
Yanhao Zhang, Zhihan Zhu, Yong Xia
Abstract
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.
BibTeX
@inproceedings{
zhang2024block,
title={Block Sparse Bayesian Learning: A Diversified Scheme},
author={Yanhao Zhang and Zhihan Zhu and Yong Xia},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=a4cPpx1xYg}
}