{"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/feature-selection-with-annealing-for-computer","title":"Feature Selection with Annealing for Computer Vision and Big Data Learning","arxiv_id":"1310.2880","date":"2013-10-10","proceeding":"IEEE Transactions on Pattern Analysis and Machine Intelligence 2017 2","authors":["Adrian Barbu","Yiyuan She","Liangjing Ding","Gary Gramajo"],"abstract":"Many computer vision and medical imaging problems are faced with learning\nfrom large-scale datasets, with millions of observations and features. In this\npaper we propose a novel efficient learning scheme that tightens a sparsity\nconstraint by gradually removing variables based on a criterion and a schedule.\nThe attractive fact that the problem size keeps dropping throughout the\niterations makes it particularly suitable for big data learning. Our approach\napplies generically to the optimization of any differentiable loss function,\nand finds applications in regression, classification and ranking. The resultant\nalgorithms build variable screening into estimation and are extremely simple to\nimplement. We provide theoretical guarantees of convergence and selection\nconsistency. In addition, one dimensional piecewise linear response functions\nare used to account for nonlinearity and a second order prior is imposed on\nthese functions to avoid overfitting. Experiments on real and synthetic data\nshow that the proposed method compares very well with other state of the art\nmethods in regression, classification and ranking while being computationally\nvery efficient and scalable.","url_abs":"http://arxiv.org/abs/1310.2880v7","url_pdf":"http://arxiv.org/pdf/1310.2880v7.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":"feature-selection-with-annealing-for-computer","repo_url":"https://github.com/barbua/FSA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"feature-selection","task_name":"feature selection"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}