DeepAI AI Chat
Log In Sign Up

An Introduction to MM Algorithms for Machine Learning and Statistical

11/12/2016
by   Hien D. Nguyen, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/12/2017

An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling

Mixture-of-experts (MoE) models are a powerful paradigm for modeling of ...
09/16/2022

Comments on "Iteratively Re-weighted Algorithm for Fuzzy c-Means"

In this comment, we present a simple alternate derivation to the IRW-FCM...
04/06/2022

A general approach to deriving diagnosability results of interconnection networks

We generalize an approach to deriving diagnosability results of various ...