Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition

de Vries, Gert-Jan; Biehl, Michael

doi:10.4230/DagSemProc.09081.4

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Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition

Authors Gert-Jan de Vries, Michael Biehl

Part of: Volume: Dagstuhl Seminar Proceedings, Volume 9081
Part of: Series: Dagstuhl Seminar Proceedings (DagSemProc)
License: Creative Commons Attribution 4.0 International license
Publication Date: 2009-06-23

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DagSemProc.09081.4.pdf

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DOI: 10.4230/DagSemProc.09081.4
URN: urn:nbn:de:0030-drops-20356

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Keywords

Learning Vector Quantization
Analysis
Facial Expression Recognition

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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.

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Gert-Jan de Vries and Michael Biehl. Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009) https://doi.org/10.4230/DagSemProc.09081.4

Author Details

Gert-Jan de Vries

Michael Biehl

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