IEEE Transactions on Signal Processing· 2016Q1· Review
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning
- 1,751citations
- Q1SCImago
- 2016year
Short summary
This paper provides a comprehensive overview of the Majorization-Minimization (MM) algorithmic framework, demonstrating its utility in deriving efficient algorithms for signal processing, communications, and machine learning.
AI-generated from the title and abstract; the full text is not read.
Key points
- The Majorization-Minimization (MM) framework provides a general method for deriving problem-driven algorithms with low computational cost.
- The paper covers MM's basic principles, convergence results, extensions, and acceleration schemes.
- Practical tools, including upper bounds for basic functions derived from Taylor expansion, convexity, and inequalities, are provided for constructing surrogate functions.
- MM is applied to a wide range of problems in signal processing, communications, and machine learning.
AI-generated from the title and abstract; the full text is not read.
Abstract
This paper gives an overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost. A general introduction of MM is presented, including a description of the basic principle and its convergence results. The extensions, acceleration schemes, and connection to other algorithmic frameworks are also covered. To bridge the gap between theory and practice, upperbounds for a large number of basic functions, derived based on the Taylor expansion, convexity, and special inequalities, are provided as ingredients for constructing surrogate functions. With the pre-requisites established, the way of applying MM to solving specific problems is elaborated by a wide range of applications in signal processing, communications, and machine learning.
The authors' abstract, as published at the source. IEEE Transactions on Signal Processing, 2016 · DOI ↗
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Field: Computational Mechanics
Computational MechanicsEngineering