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An introduction to Majorization‐Minimization algorithms for machine learning and statistical estimation
Author(s) -
Nguyen Hien D.
Publication year - 2017
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1198
Subject(s) - computer science , statistical learning , algorithm , machine learning , minification , structural risk minimization , multinomial logistic regression , multinomial distribution , artificial intelligence , support vector machine , relevance vector machine , series (stratigraphy) , empirical risk minimization , online machine learning , mathematics , active learning (machine learning) , statistics , programming language , paleontology , biology
MM (majorization–minimization) algorithms are an increasingly popular tool for solving optimization problems in machine learning and statistical estimation. This article introduces the MM algorithm framework in general and via three commonly considered example applications: Gaussian mixture regressions, multinomial logistic regressions, and support vector machines. Specific algorithms for these three examples are derived and Mathematical Programming Series A numerical demonstrations are presented. Theoretical and practical aspects of MM algorithm design are discussed. WIREs Data Mining Knowl Discov 2017, 7:e1198. doi: 10.1002/widm.1198 This article is categorized under: Algorithmic Development > Statistics Technologies > Machine Learning