{"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/no-more-meta-parameter-tuning-in-unsupervised","title":"No more meta-parameter tuning in unsupervised sparse feature learning","arxiv_id":"1402.5766","date":"2014-02-24","proceeding":null,"authors":["Adriana Romero","Petia Radeva","Carlo Gatta"],"abstract":"We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised\nfeature learning algorithm, which exploits a new way of optimizing for\nsparsity. Experiments on STL-10 show that the method presents state-of-the-art\nperformance and provides discriminative features that generalize well.","url_abs":"http://arxiv.org/abs/1402.5766v1","url_pdf":"http://arxiv.org/pdf/1402.5766v1.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":[],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-stl-10","task":"Image Classification","dataset":"STL-10","model":"No more meta-parameter tuning in unsupervised sparse feature learning","rank_in_archive_order":104,"of":117,"metrics":{"Percentage correct":"61"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}