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Incremental PCA-LDA Algorithm
Issam Dagher
Pages - 86 - 99     |    Revised - 30-04-2010     |    Published - 10-06-2010
Volume - 4   Issue - 2    |    Publication Date - May 2010  Table of Contents
Recursive PCA-LDA, principal component analysis (PCA, Face recognition
In this paper a recursive algorithm of calculating the discriminant features of the PCA-LDA procedure is introduced. This algorithm computes the principal components of a sequence of vectors incrementally without estimating the covariance matrix (so covariance-free) and at the same time computing the linear discriminant directions along which the classes are well separated. Two major techniques are used sequentially in a real time fashion in order to obtain the most efficient and linearly discriminative components. This procedure is done by merging the runs of two algorithms based on principal component analysis (PCA) and linear discriminant analysis (LDA) running sequentially. This algorithm is applied to face recognition problem. Simulation results on different databases showed high average success rate of this algorithm compared to PCA and LDA algorithms. The advantage of the incremental property of this algorithm compared to the batch PCA-LDA is also shown.
CITED BY (24)  
1 Bhardwaj, A., Gupta, A., Jain, P., Rani, A., & Yadav, J. Classification of human emotions from EEG signals using SVM and LDA Classifiers.
2 Beaulieu, P., & Megherbi, D. B. (2014, May). A study of the effect of feature reduction via statistically significant pixel selection on fruit object representation, classification, and machine learning prediction. In Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2014 IEEE International Conferenc
3 Kapoor, R., Agarwal, A., & Garg, A. (2014). A Comparative Analysis on Face Recognition Techniques.
4 Solanki, D. V., & Kothari, A. M. (2014). A Survey on Face Recognition Techniques and Its Applications. Journal of Image Processing & Pattern Recognition Progress, 1(3), 16-21.
5 Li, M., Ma, F., & Nian, F. (2014). Robust Visual Tracking via Appearance Modeling and Sparse Representation. Journal of Computers, 9(7), 1612-1619.Chicago.
6 Tartara, M. (2013). Machine learning of compiler heuristics for parallel architectures.
7 Andreu, J., & Angelov, P. (2013). An evolving machine learning method for human activity recognition systems. Journal of Ambient Intelligence and Humanized Computing, 4(2), 195-206.
8 Almohammad, M. S., Salama, G. I., & Mahmoud, T. A. (2013). Human Identification System based on Face using Active Horizontal Levels (AHLs) Feature. International Journal of Computer Applications, 61(20).
9 Patil, J. R., Bahekar, S. S., & Puri, D. D. A Survey Of Various Face Detection Algorithms.
10 Ali, A. H. A. (2013). Facial Feature Oriented Banan Filters. Journal of ACS: Advances in Computer Science, 7(NA), 23-36.
11 Almohammad, M., Salama, G., & Mahmoud, T. (2012). Human Identification System Based on Face using Active Lines Feature among Face Landmark Points. ESC Journal, 36(3).
12 He Yan, & Yufeng Qin. (2012). Based on PCA and LDA dialect identification of computer system applications, (5), 169-171.
13 Singh, A., Tiwari, S., & Singh, S. K. (2012). Performance of Face Recognition Algorithms on Dummy Faces. In Advances in Computer Science, Engineering & Applications (pp. 211-222). Springer Berlin Heidelberg.
14 Angelov, P. (2012). Autonomous Learning Systems: From Data Streams to Knowledge in Real-time. John Wiley & Sons.
15 Bhele, S. G., & Mankar, V. H. (2012). A review paper on face recognition techniques. International Journal of Advanced Research in Computer Engineering & Technology, 1(8), 339-346.
16 Bhowmik, M. K., De, B. K., Bhattacharjee, D., Basu, D. K., & Nasipuri, M. (2012, May). Multisensor fusion of visual and thermal images for human face identification using different SVM kernels. In Systems, Applications and Technology Conference (LISAT), 2012 IEEE Long Island (pp. 1-7). IEEE.
17 BHOWMIK, M. K., BHATTACHARJEE, D., BASU, D. K., & NASIPURI, M. Human Face Recognition using Wavelet Fusion and SVM.
18 Zhang, X., & Ren, X. (2011, July). Two dimensional principal component analysis based independent component analysis for face recognition. In Multimedia Technology (ICMT), 2011 International Conference on (pp. 934-936). IEEE.
19 Boschetti, A., Salgarelli, L., Muelder, C., & Ma, K. L. (2011, July). Tvi: a visual querying system for network monitoring and anomaly detection. In Proceedings of the 8th International Symposium on Visualization for Cyber Security (p. 1). ACM.
20 Baruah, R. D., Angelov, P., & Andreu, J. (2011, October). Simpl_eClass: Simplified potential-free evolving fuzzy rule-based classifiers. In Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on (pp. 2249-2254). IEEE.
21 A. Boschetti, C. Muelder, L. Salgarelli, K. L. Ma, “TVi: A Visual Querying System for Network Monitoring and Anomaly Detection” in Proceedings of the 8th International Symposium on Visualization for Cyber Security, Pittsburg, PA, USA, July 20, 2011.
22 J. Andreu and P. Angelov, “An Evolving Machine Learning Method for Human Activity Recognition Systems”, Journal of Ambient Intelligence and Humanized Computing, 2011.
23 J. Andreu, R. D. Baruah and P. Angelov, “Automatic Scene Recognition For Low-Resource Devices Using Evolving Classifiers ” in Proceedings of Fuzzy Systems (FUZZ), 2011 IEEE International Conference, Taipei, 27-30 June 2011, pp. 2779 – 2785.
24 R. D. Baruah, P. Angelov and J. Andreu, “Simpl_Eclass: Simplified Potential-Free Evolving Fuzzy Rule-Based Classifiers”, In Proceedings of Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference, Anchorage, AK, 9-12 Oct. 2011, pp. 2249 – 2254.
1 Google Scholar 
2 Academic Journals Database 
3 Academic Index 
4 CiteSeerX 
5 refSeek 
6 refSeek 
7 iSEEK 
8 ResearchGATE 
9 Libsearch 
10 Bielefeld Academic Search Engine (BASE) 
11 Scribd 
12 WorldCat 
13 SlideShare 
15 PdfSR 
16 Free-Books-Online 
. Ye, H. Xiong Q. Li, H. Park, R. Janardan, and V. Kumar.” IDR/QR: An Incremental Dimension Reduction Algorithm via QR Decomposition”, In Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pages 364–373, 2004.
A Haitao Zhao Pong Chi Yuen Kwok, J.T. “novel incremental principal component analysis and its application for face recognition Systems”, Man, and Cybernetics, Part B, IEEE Transactions on. Aug. 2006, Volume: 36, Issue: 4 On page(s): 873-886
A. M. Martinez and A. C. Kak, “PCA versus LDA”, IEEE Trans. Pattern Anal. Mach. Intell.,vol. 23, no. 2, pp. 228–233, Feb 2001
B. Raducanu, J. Vitria “Learning to learn: From smart machines to intelligent machines”, Pattern Recognition Letters 29 (2008) 1024–1032
Bioid face database, website: www.bioid.com/downloads/facedb/
D. L. Swets and J. Weng, “Using discriminant eigenfeatures for image retrieval”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp. 831–836. August 1996
Dagher, I.; Nachar, R. “Face recognition using IPCA-ICA algorithm”, Pattern Analysis and Machine Intelligence, IEEE Transactions on Volume 28, Issue 6, June 2006 Page(s):996 – 1000
Fengxi Song , Hang Liu, David Zhang, Jingyu Yang “A highly scalable incremental facial feature extraction method”, Elsevier. Neurocomputing 71 (2008) 1883– 1888
Fengxi Song, David Zhang, Qinglong Chen1, and Jingyu Yang,” A Novel Supervised Dimensionality Reduction Algorithm for Online Image Recognition” , Lecture Notes in Computer Science ; PSIVT 2006, LNCS 4319, pp. 198 – 207, 2006. Springer-Verlag.
Ghassabeh, Y.A.; Ghavami, A.; Moghaddam “A New Incremental Face Recognition System” Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2007. IDAACS 2007. 6-8 Sept. 2007 Page(s):335 – 340
H Zhao, PC Yuen, “Incremental Linear Discriminant Analysis for Face Recognition”, Systems, Man, and Cybernetics, Part B, IEEE Transactions on, Vol. 38, No. 1. (2008), pp. 210-221.
H. Murase and S.K. Nayar, “Visual Learning and Recognition of 3-D Objects from Appearance,” Int’l J. Computer Vision, vol. 14, no. 1, pp. 5-24, Jan. 1995.
H. Yu and J. Yang, “A direct LDA algorithm for high-dimensional data—With application to face Recognition” Pattern Recognition, vol. 34, pp. 2067–2070, 2001.
Haitao Zhao; Pong Chi Yuen; Kwok, J.T.; Jingyu Yang,” Incremental PCA based face Recognition”, ICARCV 2004 Dec. 2004 Page(s): 687 - 691 Vol. 1
Hakan Cevikalp, Marian Neamtu, Mitch Wilkes, and Atalay Barkana, "Discriminative Common Vectors for Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, pp.6-9, 2005.
J. J. Atick and A. N. Redlich, “What does the retina know about natural scenes?”, Neural Comput., vol. 4, pp. 196-210, 1992.
J. Karhunen and J. Joutsensalo,” Representation and separation of signals using non linear PCA type learning”, Neural Networks, 7(1),1994
J. Weng and I. Stockman, eds., Proc. NSF/DARPA Workshop Development and Learning, Apr. 2000.
K. Fukunaga, “Introduction to statistical pattern recognition”, Second ed., Academic Press, 1990
M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
M. Uray, D. Skočaj, P. Roth, H. Bischof and A. Leonardis, “Incremental LDA learning by combining reconstructive and discriminative approaches”, Proceedings of British Machine Vision Conference (BMVC ) 2007, pp. 272-281
ORL face database, website: http://www.cam-orl.co.uk/facedatabase.html, AT&T Laboratories Cambridge.
P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs Fisherfaces: recognition using class specific linear projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, 1997.
S. Chen and J. Weng, “State-Based SHOSLIF for Indoor Visual Navigation,” IEEE Trans.Neural Networks, vol. 11, no. 6, pp. 1300-1314, 2000.
Shaoning Pang, Seiichi Ozawa, Nikola Kasabov, “Chunk Incremental LDA Computing on Data Streams”, Lecture Notes in Computer Science (Advances in Neural Networks – ISNN 2005), Volume 3497/2005, pp. 51-56
Tae-Kyun Kim; Shu-Fai Wong; Stenger, B.; Kittler, J.; Cipolla, R., “Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations” Computer Vision and Pattern Recognition, 2007. IEEE Conference on, Volume , Issue , 17-22 June 2007 pp. 1-8
UMIST face database, website: http://images.ee.umist.ac.uk/danny/database.html, Daniel Graham.
Xiong H., Swamy M.N.S, and Ahmad M.O.” two dimensional FLD for face recognition” Pattern Recognition, vol 38, pp1121-1124, 2005.
Y. Cui and J. Weng, “Appearance-Base Hand Sign Recognition from Intensity Image Sequences,” Computer Vision and Image Understanding, vol. 78, pp. 157-176, 2000.
Yale face database,website:http://www1.cs.columbia.edu/~belhumeur/pub/images/yalefaces/, Colubmbia University.
Yang J, Zhang D, Frangi A.F., and Yang J.Y. “ two dimensional PCA: a new approach to appearance-based face representation and recognition” IEEE PAMI, vol 26, no 1 pp:131- 137, Jan 2004.
Ye J., Janardan R., and Li Q.,”two dimensional linear discriminant analysis”, NIPS 2004.
Yunhong Wang, Tieniu Tan and Yong Zhu, "Face Verification Based on Singular Value Decomposition and Radial Basis Function Neural Network+," Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China, 100080.
Mr. Issam Dagher
- Lebanon