Self-organizing neural networks for data projection

Mu Chun Su, Hisao Te Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


In this paper we present a nonlinear projection method for visualizing high-dimensional data as a two-dimensional scatter plot. The method is based on a new model of self-organizing neural networks. An algorithm called “double self-organizing feature map” (DSOM) algorithm is developed to train the novel model. By the DSOM algorithm the network will adaptively adjust its architecture during the learning phase so as to make neurons responding to similar stimulus be clustered together. Then the architecture of the network is graphically displayed to show the underlying structure of the data. Two data sets are used to test the effectiveness of the proposed neural network.

Original languageEnglish
Title of host publicationInternet Applications - 5th International Computer Science Conference ICSC 1999, Proceedings
EditorsLucas Chi-Kwong Hui, Dik Lun Lee
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540669035, 9783540669036
StatePublished - 1999
Event5th International Computer Science Conference, ICSC 1999 - Hong Kong, China
Duration: 13 Dec 199915 Dec 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Computer Science Conference, ICSC 1999
CityHong Kong


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