A self-growing probabilistic decision-based neural network with automatic data clustering

C. L. Tseng*, Y. H. Chen, Y. Y. Xu, Hsiao-Tien Pao, Hsin Chia Fu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

In this paper, we propose a new clustering algorithm for a mixture of Gaussian-based neural network and self-growing probabilistic decision-based neural networks (SPDNN). The proposed self-growing cluster learning (SGCL) algorithm is able to find the natural number of prototypes based on a self-growing validity measure, Bayesian information criterion (BIC). The learning process starts from a single prototype randomly initialized in the feature space and grows adaptively during the learning process until most appropriate number of prototypes are found. We have conducted numerical and real-world experiments to demonstrate the effectiveness of the SGCL algorithm. In the results of using SGCL to train the SPDNN for data clustering and speaker identification problems, we have observed a noticeable improvement among various model-based or vector quantization-based classification schemes.

Original languageEnglish
Pages (from-to)21-38
Number of pages18
JournalNeurocomputing
Volume61
Issue number1-4
DOIs
StatePublished - 1 Oct 2004

Keywords

  • Automatic data clustering
  • Bayesian information criterion
  • Self-growing probabilistic decision-based neural networks (SPDNN)
  • Supervised learning
  • Validity measure

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