{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-interactive-greedy-approach-to-group","title":"An Interactive Greedy Approach to Group Sparsity in High Dimensions","arxiv_id":"1707.02963","date":"2017-07-10","proceeding":null,"authors":["Wei Qian","Wending Li","Yasuhiro Sogawa","Ryohei Fujimaki","Xitong Yang","Ji Liu"],"abstract":"Sparsity learning with known grouping structure has received considerable\nattention due to wide modern applications in high-dimensional data analysis.\nAlthough advantages of using group information have been well-studied by\nshrinkage-based approaches, benefits of group sparsity have not been\nwell-documented for greedy-type methods, which much limits our understanding\nand use of this important class of methods. In this paper, generalizing from a\npopular forward-backward greedy approach, we propose a new interactive greedy\nalgorithm for group sparsity learning and prove that the proposed greedy-type\nalgorithm attains the desired benefits of group sparsity under high dimensional\nsettings. An estimation error bound refining other existing methods and a\nguarantee for group support recovery are also established simultaneously. In\naddition, we incorporate a general M-estimation framework and introduce an\ninteractive feature to allow extra algorithm flexibility without compromise in\ntheoretical properties. The promising use of our proposal is demonstrated\nthrough numerical evaluations including a real industrial application in human\nactivity recognition at home. Supplementary materials for this article are\navailable online.","url_abs":"http://arxiv.org/abs/1707.02963v5","url_pdf":"http://arxiv.org/pdf/1707.02963v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-interactive-greedy-approach-to-group","repo_url":"https://github.com/weiqian1/IGA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"human-activity-recognition","task_name":"Human Activity Recognition"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}