Pattern Classification: An Improvement Using Combination of VQ and PCA Based Techniques
Alok Sharma, Kuldip K. Paliwal and Godfrey C. Onwubolu
DOI : 10.3844/ajassp.2005.1445.1455
American Journal of Applied Sciences
Volume 2, Issue 10
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.
© 2005 Alok Sharma, Kuldip K. Paliwal and Godfrey C. Onwubolu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.