Block Sparse Bayesian Learning: A Diversified Scheme
This work addresses block sparsity for data analysis, but it appears incremental as it builds on existing block sparse learning methods.
The paper tackles the problem of block sparsity in real-world data by introducing a Diversified Block Sparse Prior to address sensitivity to pre-defined block information, resulting in a method (DivSBL) that shows advantages over existing algorithms in experiments.
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.