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.
@InProceedings{devries_et_al:DagSemProc.09081.4, author = {de Vries, Gert-Jan and Biehl, Michael}, title = {{Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition}}, booktitle = {Similarity-based learning on structures}, pages = {1--5}, series = {Dagstuhl Seminar Proceedings (DagSemProc)}, ISSN = {1862-4405}, year = {2009}, volume = {9081}, editor = {Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09081.4}, URN = {urn:nbn:de:0030-drops-20356}, doi = {10.4230/DagSemProc.09081.4}, annote = {Keywords: Learning Vector Quantization, Analysis, Facial Expression Recognition} }
Feedback for Dagstuhl Publishing