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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Wiley interdisciplinary reviews. Data mining and knowledge discovery Ročník 7; číslo 2; s. np - n/a
Hlavní autor: Nguyen, Hien D.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA Wiley Periodicals, Inc 01.03.2017
Wiley Subscription Services, Inc
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1942-4787
1942-4795
DOI:10.1002/widm.1198