{"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/deep-feature-selection-using-a-teacher","title":"Deep Feature Selection using a Teacher-Student Network","arxiv_id":"1903.07045","date":"2019-03-17","proceeding":null,"authors":["Ali Mirzaei","Vahid Pourahmadi","Mehran Soltani","Hamid Sheikhzadeh"],"abstract":"High-dimensional data in many machine learning applications leads to\ncomputational and analytical complexities. Feature selection provides an\neffective way for solving these problems by removing irrelevant and redundant\nfeatures, thus reducing model complexity and improving accuracy and\ngeneralization capability of the model. In this paper, we present a novel\nteacher-student feature selection (TSFS) method in which a 'teacher' (a deep\nneural network or a complicated dimension reduction method) is first employed\nto learn the best representation of data in low dimension. Then a 'student'\nnetwork (a simple neural network) is used to perform feature selection by\nminimizing the reconstruction error of low dimensional representation. Although\nthe teacher-student scheme is not new, to the best of our knowledge, it is the\nfirst time that this scheme is employed for feature selection. The proposed\nTSFS can be used for both supervised and unsupervised feature selection. This\nmethod is evaluated on different datasets and is compared with state-of-the-art\nexisting feature selection methods. The results show that TSFS performs better\nin terms of classification and clustering accuracies and reconstruction error.\nMoreover, experimental evaluations demonstrate a low degree of sensitivity to\nparameter selection in the proposed method.","url_abs":"http://arxiv.org/abs/1903.07045v1","url_pdf":"http://arxiv.org/pdf/1903.07045v1.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":"deep-feature-selection-using-a-teacher","repo_url":"https://github.com/alimirzaei/TSFS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}