Learning with Diversification from Block Sparse Signal
Yanhao Zhang Zhihan Zhu Yong Xia
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 variance and correlation matrix, 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.