DagSemProc.09081.4.pdf
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Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition
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Dagstuhl Seminar Proceedings, Volume 9081
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2009-06-23
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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
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.
Subject Classification
Keywords
- Learning Vector Quantization
- Analysis
- Facial Expression Recognition
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