{"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/warped-mixtures-for-nonparametric-cluster","title":"Warped Mixtures for Nonparametric Cluster Shapes","arxiv_id":"1408.2061","date":"2014-08-09","proceeding":null,"authors":["Tomoharu Iwata","David Duvenaud","Zoubin Ghahramani"],"abstract":"A mixture of Gaussians fit to a single curved or heavy-tailed cluster will\nreport that the data contains many clusters. To produce more appropriate\nclusterings, we introduce a model which warps a latent mixture of Gaussians to\nproduce nonparametric cluster shapes. The possibly low-dimensional latent\nmixture model allows us to summarize the properties of the high-dimensional\nclusters (or density manifolds) describing the data. The number of manifolds,\nas well as the shape and dimension of each manifold is automatically inferred.\nWe derive a simple inference scheme for this model which analytically\nintegrates out both the mixture parameters and the warping function. We show\nthat our model is effective for density estimation, performs better than\ninfinite Gaussian mixture models at recovering the true number of clusters, and\nproduces interpretable summaries of high-dimensional datasets.","url_abs":"http://arxiv.org/abs/1408.2061v1","url_pdf":"http://arxiv.org/pdf/1408.2061v1.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":"warped-mixtures-for-nonparametric-cluster","repo_url":"https://github.com/duvenaud/warped-mixtures","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"density-estimation","task_name":"Density Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1408.2061","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}