{"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/a-new-spectral-method-for-latent-variable","title":"A New Spectral Method for Latent Variable Models","arxiv_id":"1612.03409","date":"2016-12-11","proceeding":null,"authors":["Matteo Ruffini","Marta Casanellas","Ricard Gavaldà"],"abstract":"This paper presents an algorithm for the unsupervised learning of latent\nvariable models from unlabeled sets of data. We base our technique on spectral\ndecomposition, providing a technique that proves to be robust both in theory\nand in practice. We also describe how to use this algorithm to learn the\nparameters of two well known text mining models: single topic model and Latent\nDirichlet Allocation, providing in both cases an efficient technique to\nretrieve the parameters to feed the algorithm. We compare the results of our\nalgorithm with those of existing algorithms on synthetic data, and we provide\nexamples of applications to real world text corpora for both single topic model\nand LDA, obtaining meaningful results.","url_abs":"http://arxiv.org/abs/1612.03409v2","url_pdf":"http://arxiv.org/pdf/1612.03409v2.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":"a-new-spectral-method-for-latent-variable","repo_url":"https://github.com/mruffini/SpectralMethod","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}