PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection (JML)
Koji Tsuda, Gunnar Rätsch and Manfred Warmuth
Journal of Machine Learning Research 2004.

Abstract

We address the problem of learning a symmetric positive definite matrix. The central issue is to design parameter updates that preserve positive definiteness. Our updates are motivated with the von Neumann divergence. Rather than treating the most general case, we focus on two key applications that exemplify our methods: on-line learning with a simple square loss, and finding a symmetric positive definite matrix subject to linear constraints. The updates generalize the Exponentiated Gradient (EG) update and AdaBoost, respectively: the parameter is now a symmetric positive definite matrix of trace one instead of a probability vector (which in this context is a diagonal positive definite matrix with trace one). The generalized updates use matrix logarithms and exponentials to preserve positive definiteness. Most importantly, we show how the analyzes of the original EG update and AdaBoost generalize to the non-diagonal case. We apply the new versions of both, called the Matrix Exponentiated Gradient (MEG) update and DefiniteBoost, to learn a kernel matrix from distance measurements.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:745
Deposited By:Gunnar Rätsch
Deposited On:30 December 2004