An Introduction to MM Algorithms for Machine Learning and Statistical
Computation
2016-11-16 v1 Machine Learning
Machine Learning
Abstract
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 popular example applications: Gaussian mixture regressions, multinomial logistic regressions, and support vector machines. Specific algorithms for the three examples are derived and numerical demonstrations are presented. Theoretical and practical aspects of MM algorithm design are discussed.
Cite
@article{arxiv.1611.03969,
title = {An Introduction to MM Algorithms for Machine Learning and Statistical},
author = {Hien D. Nguyen},
journal= {arXiv preprint arXiv:1611.03969},
year = {2016}
}