Margin Maximizing Discriminant Analysis
- Andras Kocsor
- Kornel Kovacs
- Csaba Szepesvari, Department of Computing Science; PI of AICML
We propose a new feature extraction method called Margin Maximizing Discriminant Analysis (MMDA) which seeks to extract features suitable for classification tasks. MMDA is based on the principle that an ideal feature should convey the maximum information about the class labels and it should depend only on the geometry of the optimal decision boundary and not on those parts of the distribution of the input data that do not participate in shaping this boundary. Further, distinct feature components should convey unrelated information about the data. Two feature extraction methods are proposed for calculating the parameters of such a projection that are shown to yield equivalent results. The kernel mapping idea is used to derive non-linear versions. Experiments with several real-world, publicly available data sets demonstrate that the new method yields competitive results.
Citation
A. Kocsor, K. Kovacs, C. Szepesvari. "Margin Maximizing Discriminant Analysis". European Conference on Machine Learning (ECML), Pisa, Italy, January 2004.| Keywords: | machine learning |
| Category: | In Conference |
BibTeX
@incollection{Kocsor+al:ECML04,
author = {Andras Kocsor and Kornel Kovacs and Csaba Szepesvari},
title = {Margin Maximizing Discriminant Analysis},
booktitle = {European Conference on Machine Learning (ECML)},
year = 2004,
}Last Updated: April 24, 2007Submitted by AICML Admin Assistant
