{"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/adaptive-mixtures-of-factor-analyzers","title":"Adaptive Mixtures of Factor Analyzers","arxiv_id":"1507.02801","date":"2015-07-10","proceeding":null,"authors":["Heysem Kaya","Albert Ali Salah"],"abstract":"A mixture of factor analyzers is a semi-parametric density estimator that\ngeneralizes the well-known mixtures of Gaussians model by allowing each\nGaussian in the mixture to be represented in a different lower-dimensional\nmanifold. This paper presents a robust and parsimonious model selection\nalgorithm for training a mixture of factor analyzers, carrying out simultaneous\nclustering and locally linear, globally nonlinear dimensionality reduction.\nPermitting different number of factors per mixture component, the algorithm\nadapts the model complexity to the data complexity. We compare the proposed\nalgorithm with related automatic model selection algorithms on a number of\nbenchmarks. The results indicate the effectiveness of this fast and robust\napproach in clustering, manifold learning and class-conditional modeling.","url_abs":"http://arxiv.org/abs/1507.02801v2","url_pdf":"http://arxiv.org/pdf/1507.02801v2.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":"adaptive-mixtures-of-factor-analyzers","repo_url":"https://github.com/heysemkaya/amofa","is_official":0,"mentioned_in_paper":0,"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":"model-selection","task_name":"Model Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}