Pattern classification: An improvement using combination of VQ and PCA techniques

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Title Pattern classification: An improvement using combination of VQ and PCA techniques
Author Sharma, Alok; Paliwal, Kuldip Kumar; Onwubolu, Godfrey C.
Journal Name American Journal of Applied Sciences
Year Published 2005
Place of publication United States
Publisher Science Publications
Abstract This study firstly presents a survey on basic classifiers namely Minimum Distance Classifier (MDC), Vector Quantization (VQ), Principal Component Analysis (PCA), Nearest Neighbor (NN) and K-Nearest Neighbor (KNN). Then Vector Quantized Principal Component Analysis (VQPCA) which is generally used for representation purposes is considered for performing classification tasks. Some classifiers achieve high classification accuracy but their data storage requirement and processing time are severely expensive. On the other hand some methods for which storage and processing time are economical do not provide sufficient levels of classification accuracy. In both the cases the performance is poor. By considering the limitations involved in the classifiers we have developed Linear Combined Distance (LCD) classifier which is the combination of VQ and VQPCA techniques. The proposed technique is effective and outperforms all the other techniques in terms of getting high classification accuracy at very low data storage requirement and processing time. This would allow an object to be accurately classified as quickly as possible using very low data storage capacity.
Peer Reviewed Yes
Published Yes
Alternative URI
Volume 2
Issue Number 10
Page from 1445
Page to 1455
ISSN 1546-9239
Date Accessioned 2006-02-22
Date Available 2015-02-04T04:25:26Z
Language en_US
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Engineering and Information Technology
Subject PRE2009-Pattern Recognition
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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