Presentation 1998/3/20
Competitive Model to Classify Unknown Data into Hierarchical Clusters Through Unsupervised Learning
Yoshihisa Meki, Toshiki Kindo, Hiroaki Kurokawa, Iwao Sasase,
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Abstract(in English) We present a multi-layer competitive model to classify unknown data into hierarchical clusters through unsupervised learning. The first layer of the model is Learning Baysian Quantization (LBQ) whose distance measure is given by discriminant function of the Bayes decision. 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 subclusters, 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. We show the results of the color image processing using the four-layer MLBQ.
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Keyword(in English) competitive model / clustering / hierarchical representation / Bayes decision / image processing
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Committee NC
Conference Date 1998/3/20(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Competitive Model to Classify Unknown Data into Hierarchical Clusters Through Unsupervised Learning
Sub Title (in English)
Keyword(1) competitive model
Keyword(2) clustering
Keyword(3) hierarchical representation
Keyword(4) Bayes decision
Keyword(5) image processing
1st Author's Name Yoshihisa Meki
1st Author's Affiliation Keio University()
2nd Author's Name Toshiki Kindo
2nd Author's Affiliation PRESTO JST
3rd Author's Name Hiroaki Kurokawa
3rd Author's Affiliation Keio University
4th Author's Name Iwao Sasase
4th Author's Affiliation Keio University
Date 1998/3/20
Paper #
Volume (vol) vol.97
Number (no) 624
Page pp.pp.-
#Pages 8
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