An efficient incremental kernel principal component analysis for online feature selection

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn , Issue , 2007, Pages 2346-2351

  Takeuchi, Yohei ^a   Ozawa, Seiichi ^a   Abe, Shigeo ^a  

^a KOBE UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; EDUCATION; EIGENVALUES AND EIGENFUNCTIONS; FEATURE EXTRACTION; FINANCIAL DATA PROCESSING; LEARNING ALGORITHMS; NEURAL NETWORKS;

APPROXIMATION ACCURACY; BENCHMARK DATASETS; EIGEN SPACE; EIGENSPACES; EIGENVECTORS; EXTRACTION METHODS; HIGH-DIMENSIONAL FEATURE SPACE; JOINT CONFERENCE; KERNEL PRINCIPAL COMPONENT ANALYSIS; LEARNING TIME; ONLINE FEATURE SELECTION; TRAINING DATA;

PRINCIPAL COMPONENT ANALYSIS;

EID: 51949100797     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2007.4371325     Document Type: Conference Paper

References (14)

1. S. Abe, "Support Vector Machines for Pattern Classification," London, Springer, 2005.
2. T. J. Chin and D. Suter, "Improving the Speed of Kernel PCA on Large Scale Datasets," Proc. IEEE Int. Conf. on Video and Signal Based Surveillance, p. 41, 2006.
3. P. Hall, D. Marshall, and R. Martin, "Incremental Eigenanalysis for Classification," Department of Computer Science, University of Wales, Cardiff, UK, Research Report Serious, No. 98001, 1998.
4. I. Jolliffe, "Principal Component Analysis," New York, Springer, 1986.
5. B. J. Kim, "Active Visual Learning and Recognition Using Incremental Kernel PCA," Lecture Notes in Computer Science, Springer, Vol. 3809, pp. 585-592, 2005.
6. S. Kimura, S. Ozawa, and S. Abe, "Incremental Kernel PCA for Online Learning of Feature Space," Proc. Int. Conf. on Computational Intelligence for Modeling Control and Automation, Vol. 1, pp. 595-600, 2005.
7. R. S. Lin, M. H. Yang, and S. E. Levinson, "Object Tracking Using Incremental Fisher Discriminant Analysis," Proc. 17th Int. Conf. on Pattern Recognition, Vol. 2, pp. 757-760, 2004.
8. S. Ozawa, S. L. Toh, S. Abe, S. Pang, and N. Kasabov, "Incremental Learning of Feature Space and Classifier for Face Recognition," Neural Networks, Vol. 18, Nos. 5-6, pp. 575-584, 2005.
9. S. Pang, S. Ozawa, and N. Kasabov, "Incremental Linear Discriminant Analysis for Classification of Data Streams," IEEE Trans. on Systems, Man and Cybernetics, Part B, Vol. 35, pp. 905-914, 2005.
10. B. Schölkopf, A. Somola, and K. Müller, "Nonlinear Component Analysis as a Kernel Eigenvalue Problem," Neural Computation, Vol. 10, pp. 1299-1319, 1998.
11. D. Skočaj and A. Leonardis, "Weighted and Robust Incremental Method for Subspace Learning," Proc. Computer Vision 2003, pp. 1494-1501, 2003.
12. J. Weng, Y. Zhang, and W. S. Hwang, "Candid Covariance-free Incremental Principal Component Analysis," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 25, 2003.
13. J. Yan, N. Liu, B. Zhang, Q. Yang, S. Yan, and Z. Chen, "A Novel Scalable Algorithm for Supervised Subspace Learning," Proc. 6th IEEE Int. Conf. on Data Mining, pp. 721-730, 2006.
14. H. Zhao, P. Chi, and J. T. Kwok, "A Novel Incremental Principal Component Analysis and Its Application for Face Recognition," IEEE Trans. on Systems, Man and Cybernetics, Part B, Vol. 36, pp. 873-886, 2006.