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

Yoshihisa Meki, Toshiki Kindo, Hiroaki Kurokawa, Iwao Sasase

研究成果: Paper査読

抄録

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.

本文言語English
ページ815-818
ページ数4
出版ステータスPublished - 1997 12月 1
イベントProceedings of the 1997 6th IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. Part 1 (of 2) - Victoria, Can
継続期間: 1997 8月 201997 8月 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

ASJC Scopus subject areas

  • 信号処理
  • コンピュータ ネットワークおよび通信

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