Sciweavers

Share
ML
2007
ACM

Surrogate maximization/minimization algorithms and extensions

9 years 3 months ago
Surrogate maximization/minimization algorithms and extensions
Abstract Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. An SM algorithm aims at turning an otherwise intractable maximization problem into a tractable one by iterating two steps. The S-step computes a tractable surrogate function to substitute the original objective function and the M-step seeks to maximize this surrogate function. Convexity plays a central role in the S-step. SM algorithms enjoy the same convergence properties as EM algorithms. There are mainly three approaches to the construction of surrogate functions, namely, by using Jensen’s inequality, first-order Taylor approximation, and the low quadratic bound principle. In this paper, we demonstrate the usefulness of SM algorithms by taking logistic regression models, AdaBoost and the log-linear model as examples. More specifically, by using different surrogate function construction methods, we devis...
Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where ML
Authors Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
Comments (0)
books