{"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/correspondence-analysis-using-neural-networks","title":"Correspondence Analysis Using Neural Networks","arxiv_id":"1902.07828","date":"2019-02-21","proceeding":null,"authors":["Hsiang Hsu","Salman Salamatian","Flavio P. Calmon"],"abstract":"Correspondence analysis (CA) is a multivariate statistical tool used to\nvisualize and interpret data dependencies. CA has found applications in fields\nranging from epidemiology to social sciences. However, current methods used to\nperform CA do not scale to large, high-dimensional datasets. By re-interpreting\nthe objective in CA using an information-theoretic tool called the principal\ninertia components, we demonstrate that performing CA is equivalent to solving\na functional optimization problem over the space of finite variance functions\nof two random variable. We show that this optimization problem, in turn, can be\nefficiently approximated by neural networks. The resulting formulation, called\nthe correspondence analysis neural network (CA-NN), enables CA to be performed\nat an unprecedented scale. We validate the CA-NN on synthetic data, and\ndemonstrate how it can be used to perform CA on a variety of datasets,\nincluding food recipes, wine compositions, and images. Our results outperform\ntraditional methods used in CA, indicating that CA-NN can serve as a new,\nscalable tool for interpretability and visualization of complex dependencies\nbetween random variables.","url_abs":"http://arxiv.org/abs/1902.07828v1","url_pdf":"http://arxiv.org/pdf/1902.07828v1.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":"correspondence-analysis-using-neural-networks","repo_url":"https://github.com/HsiangHsu/2019-AISTATS-CA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"correspondence-analysis-using-neural-networks","repo_url":"https://github.com/peck94/cann-detector","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"epidemiology","task_name":"Epidemiology"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.07828","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}