An introduction to Majorization‐Minimization algorithms for machine learning and statistical estimation

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 regressi...

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Veröffentlicht in:Wiley interdisciplinary reviews. Data mining and knowledge discovery Jg. 7; H. 2; S. np - n/a
1. Verfasser: Nguyen, Hien D.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA Wiley Periodicals, Inc 01.03.2017
Wiley Subscription Services, Inc
Schlagworte:
Algorithms
Artificial intelligence
Data mining
Logistics
Machine learning
Mathematical analysis
Mathematical programming
Optimization
Regression analysis
Series (mathematics)
Support vector machines
Wire
ISSN:1942-4787, 1942-4795
Online-Zugang:Volltext
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Zusammenfassung: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 Technologies > Statistical Fundamentals A quadratic majorizer for the absolute value function.
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ISSN:1942-4787
1942-4795
DOI:10.1002/widm.1198