An Interactive Greedy Approach to Group Sparsity in High Dimensions
Abstract
Sparsity learning with known grouping structure has received considerable attention due to wide modern applications in high-dimensional data analysis. Although advantages of using group information have been well-studied by shrinkage-based approaches, benefits of group sparsity have not been well-documented for greedy-type methods, which much limits our understanding and use of this important class of methods. In this paper, generalizing from a popular forward-backward greedy approach, we propose a new interactive greedy algorithm for group sparsity learning and prove that the proposed greedy-type algorithm attains the desired benefits of group sparsity under high dimensional settings. An estimation error bound refining other existing methods and a guarantee for group support recovery are also established simultaneously. In addition, we incorporate a general M-estimation framework and introduce an interactive feature to allow extra algorithm flexibility without compromise in theoretical properties. The promising use of our proposal is demonstrated through numerical evaluations including a real industrial application in human activity recognition at home. Supplementary materials for this article are available online.
Cite
@article{arxiv.1707.02963,
title = {An Interactive Greedy Approach to Group Sparsity in High Dimensions},
author = {Wei Qian and Wending Li and Yasuhiro Sogawa and Ryohei Fujimaki and Xitong Yang and Ji Liu},
journal= {arXiv preprint arXiv:1707.02963},
year = {2018}
}