{"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-k-means-jointly-clustering-with-k-means","title":"Deep $k$-Means: Jointly clustering with $k$-Means and learning representations","arxiv_id":"1806.10069","date":"2018-06-26","proceeding":null,"authors":["Maziar Moradi Fard","Thibaut Thonet","Eric Gaussier"],"abstract":"We study in this paper the problem of jointly clustering and learning\nrepresentations. As several previous studies have shown, learning\nrepresentations that are both faithful to the data to be clustered and adapted\nto the clustering algorithm can lead to better clustering performance, all the\nmore so that the two tasks are performed jointly. We propose here such an\napproach for $k$-Means clustering based on a continuous reparametrization of\nthe objective function that leads to a truly joint solution. The behavior of\nour approach is illustrated on various datasets showing its efficacy in\nlearning representations for objects while clustering them.","url_abs":"http://arxiv.org/abs/1806.10069v2","url_pdf":"http://arxiv.org/pdf/1806.10069v2.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-k-means-jointly-clustering-with-k-means","repo_url":"https://github.com/MaziarMF/deep-k-means","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.10069","atlas_url":"https://app.syntology.ai/?focus=1806.10069","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}