{"title":"基于对称信息散度的非负矩阵分解混合算法。","authors":"Karthik Devarajan, Nader Ebrahimi, Ehsan Soofi","doi":"10.1109/BIBM.2015.7359924","DOIUrl":null,"url":null,"abstract":"<p><p>The objective of this paper is to provide a hybrid algorithm for non-negative matrix factorization based on a symmetric version of Kullback-Leibler divergence, known as <i>intrinsic information</i>. The convergence of the proposed algorithm is shown for several members of the exponential family such as the Gaussian, Poisson, gamma and inverse Gaussian models. The speed of this algorithm is examined and its usefulness is illustrated through some applied problems.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2015 ","pages":"1658-1664"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2015.7359924","citationCount":"4","resultStr":"{\"title\":\"A Hybrid Algorithm for Non-negative Matrix Factorization Based on Symmetric Information Divergence.\",\"authors\":\"Karthik Devarajan, Nader Ebrahimi, Ehsan Soofi\",\"doi\":\"10.1109/BIBM.2015.7359924\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The objective of this paper is to provide a hybrid algorithm for non-negative matrix factorization based on a symmetric version of Kullback-Leibler divergence, known as <i>intrinsic information</i>. The convergence of the proposed algorithm is shown for several members of the exponential family such as the Gaussian, Poisson, gamma and inverse Gaussian models. The speed of this algorithm is examined and its usefulness is illustrated through some applied problems.</p>\",\"PeriodicalId\":74563,\"journal\":{\"name\":\"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine\",\"volume\":\"2015 \",\"pages\":\"1658-1664\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/BIBM.2015.7359924\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBM.2015.7359924\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2015/12/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBM.2015.7359924","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2015/12/17 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
A Hybrid Algorithm for Non-negative Matrix Factorization Based on Symmetric Information Divergence.
The objective of this paper is to provide a hybrid algorithm for non-negative matrix factorization based on a symmetric version of Kullback-Leibler divergence, known as intrinsic information. The convergence of the proposed algorithm is shown for several members of the exponential family such as the Gaussian, Poisson, gamma and inverse Gaussian models. The speed of this algorithm is examined and its usefulness is illustrated through some applied problems.
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