Fair Marriage Principle and Initialization Map for the EM Algorithm
Chenguang Lu

TL;DR
This paper challenges existing EM convergence theory, reveals factors affecting convergence speed, and introduces the Channel Matching EM algorithm with an initialization map to improve global convergence in mixture models.
Contribution
It provides new insights into EM convergence, debunks the popular theory, and proposes an improved algorithm with an initialization map for better convergence speed.
Findings
The popular convergence theory of EM is incorrect.
Unfair competition between components reduces convergence speed.
The Channel Matching EM algorithm accelerates global convergence.
Abstract
The popular convergence theory of the EM algorithm explains that the observed incomplete data log-likelihood L and the complete data log-likelihood Q are positively correlated, and we can maximize L by maximizing Q. The Deterministic Annealing EM (DAEM) algorithm was hence proposed for avoiding locally maximal Q. This paper provides different conclusions: 1) The popular convergence theory is wrong; 2) The locally maximal Q can affect the convergent speed, but cannot block the global convergence; 3) Like marriage competition, unfair competition between two components may vastly decrease the globally convergent speed; 4) Local convergence exists because the sample is too small, and unfair competition exists; 5) An improved EM algorithm, called the Channel Matching (CM) EM algorithm, can accelerate the global convergence. This paper provides an initialization map with two means as two axes…
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Taxonomy
TopicsStatistical Distribution Estimation and Applications · Computational and Text Analysis Methods
