Expansive competitive learning for kernel vector quantization

Generalized Derivative Based Kernelized Learning Vector Quantization

We derive a novel derivative based version of kernelized Generalized Learning Vector Quantization (KGLVQ) as an effective, easy to interpret, prototype based and kernelized classifier. It is called D-KGLVQ and we provide generalization error bounds, experimental results on real world data, showing that D-KGLVQ is competitive with KGLVQ and the SVM on UCI data and additionally show that automati...

NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map

Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different ...

Average Competitive Learning Vector Quantization

We propose a new algorithm for vector quantization:Average Competitive Learning Vector Quantization(ACLVQ). It is a rather simple modification of the classical Competitive Learning Vector Quantization(CLVQ). This new formulation gives us similar results for the quantization error to those obtained by the CLVQ and reduce considerably the computation time to achieve the optimal quantizer. We esta...

A Note on Gradient Based Learning in Vector Quantization Using Differentiable Kernels for Hilbert and Banach Spaces

Supervised and unsupervised prototype based vector quantization frequently are proceeded in the Euclidean space. In the last years, also non-standard metrics became popular. For classification by support vector machines, Hilbert or Banach space representations are very successful based on so-called kernel metrics. In this paper we give the mathematical justification that gradient based learning...

On the critical points of the 1-dimensional competitive learning vector quantization algorithm