Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition
Gert-Jan de Vries, Michael L. Biehl · University of Groningen research database (University of Groningen / Centre for Information Technology) · 2009
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.