{"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-binary-latent-variable-models-a","title":"Learning Binary Latent Variable Models: A Tensor Eigenpair Approach","arxiv_id":"1802.09656","date":"2018-02-27","proceeding":"ICML 2018 7","authors":["Ariel Jaffe","Roi Weiss","Shai Carmi","Yuval Kluger","Boaz Nadler"],"abstract":"Latent variable models with hidden binary units appear in various\napplications. Learning such models, in particular in the presence of noise, is\na challenging computational problem. In this paper we propose a novel spectral\napproach to this problem, based on the eigenvectors of both the second order\nmoment matrix and third order moment tensor of the observed data. We prove that\nunder mild non-degeneracy conditions, our method consistently estimates the\nmodel parameters at the optimal parametric rate. Our tensor-based method\ngeneralizes previous orthogonal tensor decomposition approaches, where the\nhidden units were assumed to be either statistically independent or mutually\nexclusive. We illustrate the consistency of our method on simulated data and\ndemonstrate its usefulness in learning a common model for population mixtures\nin genetics.","url_abs":"http://arxiv.org/abs/1802.09656v1","url_pdf":"http://arxiv.org/pdf/1802.09656v1.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-binary-latent-variable-models-a","repo_url":"https://github.com/arJaffe/BinaryLatentVariables","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"tensor-decomposition","task_name":"Tensor Decomposition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.09656","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}