Competitive model to classify unknown data into hierarchical clusters through unsupervised learning

Yoshihisa Meki, Toshiki Kindo, Hiroaki Kurokawa, Iwao Sasase

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a multi-layer competitive model to classify unknown data into hierarchical clusters through unsupervised learning. The first layer of the model, which is a Winner-Takes-All type competitive network within N reference vectors, is an extended Learning Vector Quantization whose distance measure is given by discriminant function of the Bayes decision. The extended LVQ is referred to Learning Baysian Quantization (LBQ). LBQ organizes reference vectors which correspond to prototypes of input data through unsupervised learning. The second layer of the model includes N LBQs as its subnetworks. The a-th reference vector in the first layer connects to the a-th LBQ in the second layer. The a-th LBQ works as a secondary LBQ if and only if the a-th neuron excites. When input data consists of several clusters which include several sub-clusters, the presented model organizes the prototypes of the clusters as the reference vector in the first layer and those of the subclusters as the reference vector in the second layer hierarchically.

Original languageEnglish
Title of host publicationIEEE Pacific RIM Conference on Communications, Computers, and Signal Processing - Proceedings
PublisherIEEE
Pages815-818
Number of pages4
Volume2
Publication statusPublished - 1997
EventProceedings of the 1997 6th IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. Part 1 (of 2) - Victoria, Can
Duration: 1997 Aug 201997 Aug 22

Other

OtherProceedings of the 1997 6th IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. Part 1 (of 2)
CityVictoria, Can
Period97/8/2097/8/22

Fingerprint

Unsupervised learning
Vector quantization
Neurons

ASJC Scopus subject areas

  • Signal Processing

Cite this

Meki, Y., Kindo, T., Kurokawa, H., & Sasase, I. (1997). Competitive model to classify unknown data into hierarchical clusters through unsupervised learning. In IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing - Proceedings (Vol. 2, pp. 815-818). IEEE.

Competitive model to classify unknown data into hierarchical clusters through unsupervised learning. / Meki, Yoshihisa; Kindo, Toshiki; Kurokawa, Hiroaki; Sasase, Iwao.

IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing - Proceedings. Vol. 2 IEEE, 1997. p. 815-818.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Meki, Y, Kindo, T, Kurokawa, H & Sasase, I 1997, Competitive model to classify unknown data into hierarchical clusters through unsupervised learning. in IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing - Proceedings. vol. 2, IEEE, pp. 815-818, Proceedings of the 1997 6th IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. Part 1 (of 2), Victoria, Can, 97/8/20.
Meki Y, Kindo T, Kurokawa H, Sasase I. Competitive model to classify unknown data into hierarchical clusters through unsupervised learning. In IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing - Proceedings. Vol. 2. IEEE. 1997. p. 815-818
Meki, Yoshihisa ; Kindo, Toshiki ; Kurokawa, Hiroaki ; Sasase, Iwao. / Competitive model to classify unknown data into hierarchical clusters through unsupervised learning. IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing - Proceedings. Vol. 2 IEEE, 1997. pp. 815-818
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