Margin Maximizing Discriminant Analysis View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2004

AUTHORS

András Kocsor , Kornél Kovács , Csaba Szepesvári

ABSTRACT

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. More... »

PAGES

227-238

References to SciGraph publications

Book

TITLE

Machine Learning: ECML 2004

ISBN

978-3-540-23105-9
978-3-540-30115-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-30115-8_23

DOI

http://dx.doi.org/10.1007/978-3-540-30115-8_23

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016269717


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