{"title":"非负矩阵分解的散度","authors":"Jbari Olaya, Chakkor Otman","doi":"10.1109/ICDATA52997.2021.00013","DOIUrl":null,"url":null,"abstract":"The beta-divergences has been largely used in the machine learning literature. In this paper, we will go into detail about what they are, where they come from, their relation with Bregman divergence, and why they are so useful in many machine learning algorithms. In particular, Nonnegative Matrix Factorization (NMF), witch we presented as an example using Majorization-Minimization approach.","PeriodicalId":231714,"journal":{"name":"2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Beta-divergence for Nonnegative Matrix Factorization\",\"authors\":\"Jbari Olaya, Chakkor Otman\",\"doi\":\"10.1109/ICDATA52997.2021.00013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The beta-divergences has been largely used in the machine learning literature. In this paper, we will go into detail about what they are, where they come from, their relation with Bregman divergence, and why they are so useful in many machine learning algorithms. In particular, Nonnegative Matrix Factorization (NMF), witch we presented as an example using Majorization-Minimization approach.\",\"PeriodicalId\":231714,\"journal\":{\"name\":\"2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA)\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDATA52997.2021.00013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDATA52997.2021.00013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Beta-divergence for Nonnegative Matrix Factorization
The beta-divergences has been largely used in the machine learning literature. In this paper, we will go into detail about what they are, where they come from, their relation with Bregman divergence, and why they are so useful in many machine learning algorithms. In particular, Nonnegative Matrix Factorization (NMF), witch we presented as an example using Majorization-Minimization approach.
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