The α-EM algorithm: Surrogate likelihood maximization using α-logarithmic information measures

Yasuo Matsuyama

    研究成果: Article査読

    37 被引用数 (Scopus)

    抄録

    A new likelihood maximization algorithm called the α-EM algorithm (α-Expectation-Maximization algorithm) is presented. This algorithm outperforms the traditional or logarithmic EM algorithm in terms of convergence speed for an appropriate range of the design parameter α. The log-EM algorithm is a special case corresponding to α = -1. The main idea behind the α-EM algorithm is to search for an effective surrogate function or a minorizer for the maximization of the observed data's likelihood ratio. The surrogate function adopted in this paper is based upon the α-logarithm which is related to the convex divergence. The convergence speed of the α-EM algorithm is theoretically analyzed through α-dependent update matrices and illustrated by numerical simulations. Finally, general guidelines for using the α-logarithmic methods are given. The choice of alternative surrogate functions is also discussed.

    本文言語English
    ページ(範囲)692-706
    ページ数15
    ジャーナルIEEE Transactions on Information Theory
    49
    3
    DOI
    出版ステータスPublished - 2003 3

    ASJC Scopus subject areas

    • Information Systems
    • Electrical and Electronic Engineering

    フィンガープリント 「The α-EM algorithm: Surrogate likelihood maximization using α-logarithmic information measures」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

    引用スタイル