DagSemProc.09081.4.pdf
- Filesize: 166 kB
- 5 pages
Document
Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition
Authors
-
Part of:
Volume:
Dagstuhl Seminar Proceedings, Volume 9081
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication date: 2009-06-23
Cite AsGet BibTex
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
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.
Keywords
- Learning Vector Quantization
- Analysis
- Facial Expression Recognition
Metrics
- Access Statistics
-
Total Accesses (updated on a weekly basis)
0PDF Downloads