NDLI: A Supervised Clustering Algorithm for the Initialization of RBF Neural Network Classifiers

Content Provider	IEEE Xplore Digital Library
Author	Cevikalp, H. Larlus, D. Jurie, F.
Copyright Year	2007
Description	Author affiliation: INRIA Rhone-Alpes, Montbonnot, France (Cevikalp, H.; Larlus, D.; Jurie, F.)
Abstract	In this paper, we propose a new supervised clustering algorithm, coined as the homogeneous clustering (HC), to find the number and initial locations of the hidden units in radial basis function (RBF) neural network classifiers. In contrast to the traditional clustering algorithms introduced for this goal, the proposed algorithm is a supervised procedure where the number and initial locations of the hidden units are determined based on split of the clusters having overlaps among the classes. The basic idea of the proposed approach is to create class specific homogenous clusters where the corresponding samples are closer to their mean than the means of rival clusters belonging to other class categories. We tested the proposed clustering algorithm along with the RBF network classifier on the Graz02 object database and the ORL face database. The experimental results show that the RBF network classifier performs better when it is initialized with the proposed HC algorithm than an unsupervised k-means algorithm. Moreover, our recognition results exceed the best published results on the Graz02 database and they are comparable to the best results on the ORL face database indicating that the proposed clustering algorithm initializes the hidden unit parameters successfully.
Starting Page	1
Ending Page	4
File Size	1075085
Page Count	4
File Format	PDF
ISBN	1424407192
DOI	10.1109/SIU.2007.4298803
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-11
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Databases Face recognition Neural networks Clustering algorithms Radial basis function networks Artificial neural networks Function approximation Erbium Testing
Content Type	Text
Resource Type	Article

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