PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A simple iterative algorithm for parsimonious binary kernel Fisher discrimination
Robert F. Harrison and Kitsuchart Pasupa
Pattern Analysis & Applications Volume 13, Number 1, pp. 15-22, 2009. ISSN 1433-7541

Abstract

By applying recent results in optimisation theory variously known as optimisation transfer or majorise/minimise algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimisation that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5629
Deposited By:Kitsuchart Pasupa
Deposited On:08 March 2010