NDLI: Sample Clustering for Fast Classification by Using the Mean Shift Procedure

Content Provider	IEEE Xplore Digital Library
Author	Liang Lie-quan Liang Ying-hong
Copyright Year	2009
Abstract	Most classification methods are limited by speed particularly when the training data set is large, such as artificial neural networks (ANNs) and support vector machines (SVMs). In this article, we explore the possibility of utilizing the Mean Shift algorithm, which is a mode seeking procedure that estimates the gradient of the data density, to decrease the sample size. We found that in a large number of samples to be trained, most samples can be clustered into a small number of mode centroids (extreme values of density), therefore, the original samples can be reduced by means of using the results of the Mean Shift procedure. To verify the validity of this method, several classifiers including the linear discriminant analysis (LDA), k nearest neighbor (kNN) and SVMs have been tested. Experimental results prove that when the parameters are selected appropriately, the proposed method is capable of reducing the computational complexity of above classification methods, with minimum effects on the classification accuracy.
Starting Page	179
Ending Page	183
File Size	407902
Page Count	5
File Format	PDF
ISBN	9780769536439
DOI	10.1109/ISECS.2009.72
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	mean shift classification methods mode seeking sample selection Electronic commerce sample reductio Support vector machines Support vector machine classification Training data Clustering algorithms Iterative algorithms Linear discriminant analysis Kernel Business Testing
Content Type	Text
Resource Type	Article

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