Learning vector quantization with adaptive prototype addition and removal

Authors:
Mihajlo Grbovic;Slobodan Vucetic
Affiliations:
Center for Information Science and Technology, Department of Computer and Information Sciences, Temple University, Philadelphia, PA;Center for Information Science and Technology, Department of Computer and Information Sciences, Temple University, Philadelphia, PA
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 13
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Interactively Exploring Hierarchical Clustering Results

Computer
Generalized relevance learning vector quantization

Neural Networks - New developments in self-organizing maps
Soft learning vector quantization

Neural Computation
A Novel Kernel Prototype-Based Learning Algorithm

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Supervised Neural Gas with General Similarity Measure

Neural Processing Letters
Alternative learning vector quantization

Pattern Recognition
Dynamics and Generalization Ability of LVQ Algorithms

The Journal of Machine Learning Research
Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis

Neurocomputing
Rapid and brief communication: Initialization insensitive LVQ algorithm based on cost-function adaptation

Pattern Recognition
A global optimization technique for statistical classifier design

IEEE Transactions on Signal Processing
Bankruptcy analysis with self-organizing maps in learning metrics

IEEE Transactions on Neural Networks

Learning vector quantization for variable ordering in constraint satisfaction problems

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Learning Vector Quantization (LVQ) is a popular class of nearest prototype classifiers for multiclass classification. Learning algorithms from this family are widely used because of their intuitively clear learning process and ease of implementation. They run efficiently and in many cases provide state of the art performance. In this paper we propose a modification of the LVQ algorithm that addresses problems of determining appropriate number of prototypes, sensitivity to initialization, and sensitivity to noise in data. The proposed algorithm allows adaptive addition of prototypes at potentially beneficial locations and removal of harmful or less useful prototypes. The prototype addition and removal steps can be easily implemented on top of many existing LVQ algorithms. Experimental results on synthetic and benchmark datasets showed that the proposed modifications can significantly improve LVQ classification accuracy while at the same time determining the appropriate number of prototypes and avoiding the problems of initialization.