Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 429451, 21 pages
http://dx.doi.org/10.1155/2014/429451
Research Article

EVD Dualdating Based Online Subspace Learning

1School of Aeronautics and Astronautics, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China
2School of Information Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China

Received 10 April 2014; Accepted 25 June 2014; Published 24 July 2014

Academic Editor: Yan Liang

Copyright © 2014 Bo Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. I. T. Jolliffe, Principal Component Analysis, Springer, New York, NY, USA, 2nd edition, 2002. View at MathSciNet
  2. K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, 1990. View at MathSciNet
  3. M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 1991. View at Publisher · View at Google Scholar · View at Scopus
  4. K. Y. Yeung and W. L. Ruzzo, “Principal component analysis for clustering gene expression data,” Bioinformatics, vol. 17, no. 9, pp. 763–774, 2001. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, “Indexing by latent semantic analysis,” Journal of American Society for Information Science, vol. 41, no. 6, pp. 391–407, 1990. View at Google Scholar
  6. X. Li, W. Hu, C. Shen, Z. Zhang, A. Dick, and A. van den Hengel, “A survey of appearance models in visual object tracking,” ACM Transactions on Intelligent Systems and Technology, vol. 4, no. 4, article 58, 2013. View at Publisher · View at Google Scholar
  7. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995. View at MathSciNet
  8. G. Cauwenberghs and T. Poggio, “Incremental and decremental support vector machine learning,” Advances in Neural Information Processing Systems, pp. 100–110, 2000. View at Google Scholar
  9. M. Karasuyama and I. Takeuchi, “Multiple incremental decremental learning of support vector machines,” IEEE Transactions on Neural Networks, vol. 21, no. 7, pp. 1048–1059, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. F. Lorenzelli and K. Yao, Systolic Arrays for SVD Downdating, SVD and Signal Processing III: Algorithms, Architectures, and Applications, Elsevier Science, 1995.
  11. P. Hall, D. Marshall, and R. Martin, “Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition,” Image and Vision Computing, vol. 20, no. 13-14, pp. 1009–1016, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Hall, D. Marshall, and R. Martin, “Merging and splitting eigenspace models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 9, pp. 1042–1049, 2000. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Skočaj and A. Leonardis, “Weighted and robust incremental method for subspace learning,” in Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV '03), vol. 2, pp. 1494–1501, Nice, France, October 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Levy and M. Lindenbaum, “Sequential karhunen-loeve basis extraction and its applicat ion to images,” IEEE Transactions on Image Processing, vol. 9, no. 8, pp. 1371–1374, 2000. View at Publisher · View at Google Scholar · View at Scopus
  15. D. Huang, Z. Yi, and X. Pu, “A new incremental PCA algorithm with application to visual learning and recognition,” Neural Processing Letters, vol. 30, no. 3, pp. 171–185, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. D. A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, “Incremental learning for robust visual tracking,” International Journal of Computer Vision, vol. 77, no. 1–3, pp. 125–141, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Weng, Y. Zhang, and W. Hwang, “Candid covariance-free incremental principal component analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 8, pp. 1034–1040, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. H. Zha and H. D. Simon, “On updating problems in latent semantic indexing,” SIAM Journal on Scientific Computing, vol. 21, no. 2, pp. 782–791, 1999. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. A. Levy and M. Lindenbaum, “Sequential Karhunen-Loeve basis extraction and its application to images,” in Proceedings of the International Conference on Image Processing (ICIP '98), vol. 2, pp. 456–460, Chicago, Ill, USA, October 1998. View at Publisher · View at Google Scholar
  20. H. Zhao, P. C. Yuen, and J. T. Kwok, “A novel incremental principal component analysis and its application for face recognition,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 36, no. 4, pp. 873–886, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Brand, “Fast low-rank modifications of the thin singular value decomposition,” Linear Algebra and its Applications, vol. 415, no. 1, pp. 20–30, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. J. Melenchón and E. Martínez, “Efficiently downdating, composing and splitting singular value decompositions preserving the mean information,” in Proceedings of the 3rd Iberian Conference on Pattern Recognition and Image Analysis, pp. 436–443, 2007.
  23. L. Wang, S. Chen, and C. Wu, “An accurate incremental principal component analysis method with capacity of update and downdate,” Proceedings of International Conference on Computer Science & Information Technology, vol. 51, pp. 118–123, 2012. View at Google Scholar
  24. M. Artac, M. Jogan, and A. Leonardis, “Incremental pca for on-line visual learning and recognition,” in Proceedings of the 16th International Conference on Pattern Recognition, vol. 3, pp. 781–784, 2002.
  25. P. Jonathon Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, “The FERET evaluation methodology for face-recognition algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1090–1104, 2000. View at Publisher · View at Google Scholar · View at Scopus
  26. A. M. Martinez and R. Benavente, “The AR face database,” CVC Technical Report 24, 1998. View at Google Scholar
  27. A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: illumination cone models for face recognition under variable lighting and pose,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 643–660, 2001. View at Publisher · View at Google Scholar · View at Scopus
  28. S. A. Nene, S. K. Nayar, and H. Murase, “Columbia object image library (COIL-100),” Tech. Rep. CUCS-006-96, 1996. View at Google Scholar
  29. S. Ozawa, S. Pang, and N. Kasabov, “Incremental learning of chunk data for online pattern classification systems,” IEEE Transactions on Neural Networks, vol. 19, no. 6, pp. 1061–1074, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. N. Goel, G. Bebis, and A. Nefian, “Face recognition experiments with random projection,” in 2nd Biometric Technology for Human Identification, vol. 5779 of Proceedings of SPIE, pp. 426–437, Orlando, Fla, USA, March 2005. View at Publisher · View at Google Scholar
  31. S. Dasgupta and A. Gupta, “An elementary proof of a theorem of Johnson and Lindenstrauss,” Random Structures & Algorithms, vol. 22, no. 1, pp. 60–65, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus