{"610337":{"#nid":"610337","#data":{"type":"event","title":"ISyE Seminar - Madeleine Udell","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E Big Data is Low Rank\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E Matrices of low rank are pervasive in big data, appearing in recommender systems, movie preferences, topic models, medical records, and genomics.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWhile there is a vast literature on how to exploit low rank structure in these datasets, there is less attention on explaining why low rank structure appears in the first place.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn this talk, we explain the abundance of low rank matrices in big data by proving that certain latent variable models associated to piecewise analytic functions are of log-rank. Any large matrix from such a latent variable model can be approximated, up to a small error, by a low rank matrix.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EArmed with this theorem, we show how to use a low rank modeling framework to exploit low rank structure even for datasets that are not numeric, with applications in the social sciences, medicine, retail, and machine learning.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E Big Data is Low Rank\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E Matrices of low rank are pervasive in big data, appearing in recommender systems, movie preferences, topic models, medical records, and genomics.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWhile there is a vast literature on how to exploit low rank structure in these datasets, there is less attention on explaining why low rank structure appears in the first place.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn this talk, we explain the abundance of low rank matrices in big data by proving that certain latent variable models associated to piecewise analytic functions are of log-rank. Any large matrix from such a latent variable model can be approximated, up to a small error, by a low rank matrix.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EArmed with this theorem, we show how to use a low rank modeling framework to exploit low rank structure even for datasets that are not numeric, with applications in the social sciences, medicine, retail, and machine learning.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Big Data is Low Rank"}],"uid":"34547","created_gmt":"2018-08-24 16:27:32","changed_gmt":"2018-10-26 16:01:15","author":"nhendricks6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2018-10-31T14:30:00-04:00","event_time_end":"2018-10-31T15:30:00-04:00","event_time_end_last":"2018-10-31T15:30:00-04:00","gmt_time_start":"2018-10-31 18:30:00","gmt_time_end":"2018-10-31 19:30:00","gmt_time_end_last":"2018-10-31 19:30:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}