Digital image processing. Multi feature face recognition in PSO -SVM
|International Journal of Electrical and Electronics Engineering|
|© 2014 by SSRG - IJEEE Journal|
|Volume 1 Issue 3|
|Year of Publication : 2014|
|Authors : R Sindhoori|
R Sindhoori, "Digital image processing. Multi feature face recognition in PSO -SVM" SSRG International Journal of Electrical and Electronics Engineering 1.3 (2014): 1-6.
R Sindhoori,(2014). Digital image processing. Multi feature face recognition in PSO -SVM. SSRG International Journal of Electrical and Electronics Engineering 1(1), 1-6.
The Support Vector Machine is a discriminative classifier which has achieved impressive results in several tasks. Classification accuracy is one of the metric to evaluate the performance of the method. However, the SVM training and testing times increases with increasing the amounts of data in the dataset. One well known approach to reduce computational expenses of SVM is the dimensionality reduction. Most of the real time data are non- linear. In this paper, F- score analysis is used for performing dimensionality reduction for non – linear data efficiently. F- score analysis is done for datasets of insurance Bench Mark Dataset, Spam dataset, and cancer dataset. The classification Accuracy is evaluated by using confusion matrix. The result shows the improvement in the performance by increasing the accuracy of the classification.
 Zizhu Fan., "Local Linear Discriminant Analysis Framework Using Sample Neighbors", IEEE Transactions on Neural Networks, , On page(s): 1119 - 1132 Volume: 22, July 2011
 Quanxue Gao., "Joint Global and Local Structure Discriminant Analysis",IEEE Transactions on Information Forensics and Security, page(s): 626 - 635 Volume: 8 April 2013
 Jieping Ye; Qi Li, “A two-stage linear discriminant analysis via QRdecomposition”, IEEE Transactions on Pattern Analysis and Machine Intelligence Volume: 27 , Issue: 6 Publication Year: 2009 , Page(s): 929 –941
 Jing Peng,; Riedel, N., "Discriminant Learning Analysis“ , IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), Volume.38, 2011.
 Yuxi Hou; Iickho Song; Hwang-Ki Min; Cheol Hoon Park , “ ComplexityReduced Scheme for Feature Extraction With Linear Discriminant Analysis“ , IEEE Transactions on Neural Networks and Learning Systems, page(s):1003 - 1009 Volume: 23, Issue: 6, June 2012
 Bin Zou; Luoqing Li; Zongben Xu; Tao Luo; Yuan Yan Tang , “Generalization Performance of Fisher Linear Discriminant Based on Markov Sampling “ , IEEE Transactions on Neural Networks and Learning Systems, page(s): 288 - 300 Volume: 24, February : 2013
 Y. Zhang and D. Y. Yeung, “Semisupervised generalized discriminant analysis “ , IEEE Transaction on Neural Networks, volume. 22, pages.1207 -1217 publication year : 2011
 Xu Chunming; Jiang Haibo ; Yu Jianjiang ,”Robust two-dimensional principle component analysis “, IEEE transaction on signals and control Publication Year: 2010 , Page(s): 452 - 455
 Ran He; Bao-Gang Hu; Xiang-Wei Kong ,"Robust Principal Component Analysis Based on Maximum Correntropy Criterion", IEEE Transactions on Image Processing,On page(s): 1485 - 1494 Volume: 20, Issue: 6, June 2011
 N. Kwak, "Principal component analysis based on L1-norm maximization", IEEE Transaction on Pattern Analysis and Machine Intelligence, volume. 30, no. 9, pages.1672 -1680 Year : 2008.
 Peng Toa, Huang Yi.et.al, “A method based on weighted F-score and SVM for feature selection”, IEEE Transaction on Pattern Analysis and Machince Intelligence. Volume. 33, May 2013.
 Qing Tao ; Dejun Chu ; Jue Wang, “Recursive Support Vector Machines for Dimensionality Reduction “,IEEE Transactions on Neural Networks, Volume: 19 , Issue: 1 Publication Year: 2008 , Page(s): 189 – 193
 J. A. Gualtieri, S. R. Chettri, R. F. Cromp, and L. F. Johnson, “Support vector machine classifiers applied to AVIRIS data,” in Proc.Summaries 8th JPL Airborne Earth Sci. Workshop, 1999, pp. 217–227, JPL Pub. 99-17.
 Z. Chen and H. Tang, “Sparse Bayesian approach to classification,” in Proc. IEEE Netw., Sens. Control, 2005, pp. 914–917.
 V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
 C. J. C. Burges, “A tutorial on support vectormachines for pattern recognition,” Data Mining Knowl. Discov., vol. 2, no. 2, pp. 121–167, Jun.1998.
 Set of tutorials on SVM’s and kernel methods. [Online]. Available:http://www.kernel-machines.org/tutorial.html
 I. El-Naqa, Y. Yang, M. N. Wernick, N. P. Galatsanos, and R. M.Nishikawa, “A support vector machine approach for detection of
microcalcifications,” IEEE Trans. Med. Imag., vol. 21, no. 12, pp. 1552–1563, Dec. 2002.
 J. Robinson and V. Kecman, “Combining support vector machine learning with the discrete cosine transform in image compression,” IEEE Trans.
Neural Netw., vol. 14, no. 4, pp. 950–958, Jul. 2003.
 M. Pontil and A. Verri, “Support vector machines for 3D object recognition,” IEEE Trans. Pattern Anal.Mach. Intell., vol. 20, no. 6, pp.
637– 646, Jun. 1998.
Support Vector Machine, Dimensionality Reduction, F- score Analysis, Confusion Matrix.