de Vries, Gert-Jan ;
Biehl, Michael
Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition
Abstract
Learning Vector Quantization (LVQ) is a popular method for multiclass classification. Several variants of LVQ have been developed recently, of which Robust Soft Learning Vector Quantization (RSLVQ) is a promising one. Although LVQ methods have an intuitive design with clear updating rules, their dynamics are not yet well understood.
In simulations within a controlled environment RSLVQ performed very close to optimal. This controlled environment enabled us to perform a mathematical analysis as a first step in obtaining a better theoretical understanding of the learning dynamics. In this talk I will discuss the theoretical analysis and its results. Moreover, I will focus on the practical application of RSLVQ to a real world dataset containing extracted features from facial expression data.
BibTeX - Entry
@InProceedings{devries_et_al:DSP:2009:2035,
author = {Gert-Jan de Vries and Michael Biehl},
title = {Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition},
booktitle = {Similarity-based learning on structures},
year = {2009},
editor = {Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
number = {09081},
series = {Dagstuhl Seminar Proceedings},
ISSN = {1862-4405},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2009/2035},
annote = {Keywords: Learning Vector Quantization, Analysis, Facial Expression Recognition}
}
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Keywords: |
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Learning Vector Quantization, Analysis, Facial Expression Recognition |
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Seminar: |
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09081 - Similarity-based learning on structures
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Issue date: |
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2009 |
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Date of publication: |
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2009 |
2009
