List of Journals    /    Call For Papers    /    Subscriptions    /    Login
 
 
 
 
 SEARCH
By Author By Title
 
 
ABOUT CSC
 About CSC Journals
 CSC Journals Objectives
 List of Journals
 CALL FOR PAPERS
 Call For Papers CFP
 Special Issue CFP
AUTHOR GUIDELINES
 Submission Guidelines
 Peer Review Process
 Helpful Hints For Getting Published
 Plagiarism Policies
 Abstracting & Indexing
 Open Access Policy
 Submit Manuscript
 FOR REVIEWERS
 Reviewer Guidelines
 FOR EDITORIAL
 Editor Guidelines
 Join Us As Editor
 Launch Special Issue
 Suggest New Journal
 CSC LIBRARY
 Browse CSC Library
 Open Access Policy
  SERVICES
 Conference Partnership Program (CPP)
 Abstracting & Indexing
 SUBSCRIPTIONS
 Subscriptions
 Discounted Packages
 Archival Subscriptions
 How to Subscribe
 Librarians
 Subscriptions Agents
 Order Form
 DOWNLOADS
 
 
 
 
Incremental PCA-LDA Algorithm
Full text
 PDF(1.68MB)
Source 
International Journal of Biometrics and Bioinformatics (IJBB)
Table of Contents
Download Complete Issue    PDF(9.12MB)
Volume:  4    Issue:  2
Pages:  13-99
Publication Date:   May 2010
ISSN (Online): 1985-2347
Pages 
86 - 99
Author(s)  
Issam Dagher - Lebanon
 
Published Date   
10-06-2010 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Recursive PCA-LDA, principal component analysis (PCA, Face recognition 
 
 
This Manuscript is indexed in the following databases/websites:-
1. Free-Books-Online
2. Directory of Open Access Journals (DOAJ)
3. Scribd
4. Docstoc
5. PDFCAST
6. Google Scholar
7. WorldCat
8. CiteSeerX
9. refSeek
10. refSeek
11. Academic Index
12. ResearchGATE
13. Bielefeld Academic Search Engine (BASE)
14. iSEEK
15. Academic Journals Database
16. Libsearch
17. slideshare
 
 
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. 
 
 
 
1 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.
2 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
3 . 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.
4 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
5 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
6 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
7 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
8 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.
9 Fengxi Song , Hang Liu, David Zhang, Jingyu Yang “A highly scalable incremental facial feature extraction method”, Elsevier. Neurocomputing 71 (2008) 1883– 1888
10 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
11 B. Raducanu, J. Vitria “Learning to learn: From smart machines to intelligent machines”, Pattern Recognition Letters 29 (2008) 1024–1032
12 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
13 J. Karhunen and J. Joutsensalo,” Representation and separation of signals using non linear PCA type learning”, Neural Networks, 7(1),1994
14 K. Fukunaga, “Introduction to statistical pattern recognition”, Second ed., Academic Press, 1990
15 J. J. Atick and A. N. Redlich, “What does the retina know about natural scenes?”, Neural Comput., vol. 4, pp. 196-210, 1992.
16 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
17 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.
18 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.
19 S. Chen and J. Weng, “State-Based SHOSLIF for Indoor Visual Navigation,” IEEE Trans.Neural Networks, vol. 11, no. 6, pp. 1300-1314, 2000.
20 J. Weng and I. Stockman, eds., Proc. NSF/DARPA Workshop Development and Learning, Apr. 2000.
21 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
22 M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
23 ORL face database, website: http://www.cam-orl.co.uk/facedatabase.html, AT&T Laboratories Cambridge.
24 UMIST face database, website: http://images.ee.umist.ac.uk/danny/database.html, Daniel Graham.
25 Yale face database,website:http://www1.cs.columbia.edu/~belhumeur/pub/images/yalefaces/, Colubmbia University.
26 Bioid face database, website: www.bioid.com/downloads/facedb/
27 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.
28 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.
29 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.
30 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.
31 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.
32 Ye J., Janardan R., and Li Q.,”two dimensional linear discriminant analysis”, NIPS 2004.
33 Xiong H., Swamy M.N.S, and Ahmad M.O.” two dimensional FLD for face recognition” Pattern Recognition, vol 38, pp1121-1124, 2005.
 
 
 
1 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.
2 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.
3 J. Andreu and P. Angelov, “An Evolving Machine Learning Method for Human Activity Recognition Systems”, Journal of Ambient Intelligence and Humanized Computing, 2011.
4 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.
 
 
 
1 2DIX
 
2 MENDELEY
 
3 pipl
 
4 lw20
 
5 Search-Document
 
6 PIGPDF.com
 
7 IEEEXplore
 
 
 
Issam Dagher : Colleagues  
 
 
 
  Untitled Document
 
Copyrights (c) 2012 Computer Science Journals. All rights reserved.
Best viewed at 1152 x 864 resolution. Microsoft Internet Explorer.
 
  
 
Copyrights & Usage: Articles published by CSC Journals are Open Access. Permission to copy and distribute any other content, images, animation and other parts of this website is prohibited. CSC Journals has the rights to take action against individual/group if they are found victim of copying these parts of the website.