Paris V. Giampouras, A. Rontogiannis, K. Themelis, K. Koutroumbas
{"title":"Online Bayesian low-rank subspace learning from partial observations","authors":"Paris V. Giampouras, A. Rontogiannis, K. Themelis, K. Koutroumbas","doi":"10.1109/EUSIPCO.2015.7362840","DOIUrl":null,"url":null,"abstract":"Learning the underlying low-dimensional subspace from streaming incomplete high-dimensional observations data has attracted considerable attention lately. In this paper, we present a new computationally efficient Bayesian scheme for online low-rank subspace learning and matrix completion. The proposed scheme builds upon a properly defined hierarchical Bayesian model that explicitly imposes low rank to the latent subspace by assigning sparsity promoting Student-t priors to the columns of the subspace matrix. The new algorithm is fully automated and as corroborated by numerical simulations, provides higher estimation accuracy and a better estimate of the true subspace rank compared to state of the art methods.","PeriodicalId":401040,"journal":{"name":"2015 23rd European Signal Processing Conference (EUSIPCO)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 23rd European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EUSIPCO.2015.7362840","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Learning the underlying low-dimensional subspace from streaming incomplete high-dimensional observations data has attracted considerable attention lately. In this paper, we present a new computationally efficient Bayesian scheme for online low-rank subspace learning and matrix completion. The proposed scheme builds upon a properly defined hierarchical Bayesian model that explicitly imposes low rank to the latent subspace by assigning sparsity promoting Student-t priors to the columns of the subspace matrix. The new algorithm is fully automated and as corroborated by numerical simulations, provides higher estimation accuracy and a better estimate of the true subspace rank compared to state of the art methods.