{"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/general-latent-feature-models-for","title":"General Latent Feature Models for Heterogeneous Datasets","arxiv_id":"1706.03779","date":"2017-06-12","proceeding":null,"authors":["Isabel Valera","Melanie F. Pradier","Maria Lomeli","Zoubin Ghahramani"],"abstract":"Latent feature modeling allows capturing the latent structure responsible for\ngenerating the observed properties of a set of objects. It is often used to\nmake predictions either for new values of interest or missing information in\nthe original data, as well as to perform data exploratory analysis. However,\nalthough there is an extensive literature on latent feature models for\nhomogeneous datasets, where all the attributes that describe each object are of\nthe same (continuous or discrete) nature, there is a lack of work on latent\nfeature modeling for heterogeneous databases. In this paper, we introduce a\ngeneral Bayesian nonparametric latent feature model suitable for heterogeneous\ndatasets, where the attributes describing each object can be either discrete,\ncontinuous or mixed variables. The proposed model presents several important\nproperties. First, it accounts for heterogeneous data while keeping the\nproperties of conjugate models, which allow us to infer the model in linear\ntime with respect to the number of objects and attributes. Second, its Bayesian\nnonparametric nature allows us to automatically infer the model complexity from\nthe data, i.e., the number of features necessary to capture the latent\nstructure in the data. Third, the latent features in the model are\nbinary-valued variables, easing the interpretability of the obtained latent\nfeatures in data exploratory analysis. We show the flexibility of the proposed\nmodel by solving both prediction and data analysis tasks on several real-world\ndatasets. Moreover, a software package of the GLFM is publicly available for\nother researcher to use and improve it.","url_abs":"http://arxiv.org/abs/1706.03779v2","url_pdf":"http://arxiv.org/pdf/1706.03779v2.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":"general-latent-feature-models-for","repo_url":"https://github.com/ivaleraM/GLFM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.03779","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}