{"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/learning-for-multi-type-subspace-clustering","title":"Learning for Multi-Type Subspace Clustering","arxiv_id":"1904.02075","date":"2019-04-03","proceeding":null,"authors":["Xun Xu","Loong-Fah Cheong","Zhuwen Li"],"abstract":"Subspace clustering has been extensively studied from the\nhypothesis-and-test, algebraic, and spectral clustering based perspectives.\nMost assume that only a single type/class of subspace is present.\nGeneralizations to multiple types are non-trivial, plagued by challenges such\nas choice of types and numbers of models, sampling imbalance and parameter\ntuning. In this work, we formulate the multi-type subspace clustering problem\nas one of learning non-linear subspace filters via deep multi-layer perceptrons\n(mlps). The response to the learnt subspace filters serve as the feature\nembedding that is clustering-friendly, i.e., points of the same clusters will\nbe embedded closer together through the network. For inference, we apply\nK-means to the network output to cluster the data. Experiments are carried out\non both synthetic and real world multi-type fitting problems, producing\nstate-of-the-art results.","url_abs":"http://arxiv.org/abs/1904.02075v1","url_pdf":"http://arxiv.org/pdf/1904.02075v1.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":"learning-for-multi-type-subspace-clustering","repo_url":"https://github.com/alex-xun-xu/LearnSubspaceMoSeg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"type","task_name":"Vocal Bursts Type Prediction"}],"methods":[{"method_slug":"spectral-clustering","method_name":"Spectral Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}