An Interactive Greedy Approach to Group Sparsity in High Dimensions

Wei Qian; Wending Li; Yasuhiro Sogawa; Ryohei Fujimaki; Xitong Yang; Ji Liu

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arXiv · 1707.02963

An Interactive Greedy Approach to Group Sparsity in High Dimensions

Wei Qian·Wending Li·Yasuhiro Sogawa·Ryohei Fujimaki·Xitong Yang·Ji Liu

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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.

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Wei Qian, Wending Li, Yasuhiro Sogawa, Ryohei Fujimaki, Xitong Yang, Ji Liu. 2017-07-10. An Interactive Greedy Approach to Group Sparsity in High Dimensions. https://arxiv.org/abs/1707.02963

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