Constructing sparse KFDA using pre-image reconstruction

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6444 LNCS, Issue PART 2, 2010, Pages 658-667

  Zhang, Qing ^a   Li, Jianwu ^b  

^a INSTITUTE OF SCIENTIFIC AND TECHNICAL INFORMATION OF CHINA   (China)

^b BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

kernel method; KFDA; pre image reconstruction; sparse approximation framework

Indexed keywords

APPROXIMATE MODEL; CLASSIFICATION ACCURACY; CLASSIFICATION EFFICIENCY; FEATURE SPACE; KERNEL FISHER DISCRIMINANT ANALYSIS; KERNEL METHODS; KERNEL TRICK; KFDA; LOCAL OPTIMAL; PRE-IMAGE RECONSTRUCTION; REAL-TIME APPLICATION; SPARSE APPROXIMATIONS; SPARSE REPRESENTATION; TEST ACCURACY; TRAINING EXAMPLE;

DATA PROCESSING; DISCRIMINANT ANALYSIS; FISHER INFORMATION MATRIX; IMAGE RECONSTRUCTION; LEARNING ALGORITHMS; IMAGE PROCESSING;

FACE RECOGNITION; IMAGE RECONSTRUCTION;

EID: 78650214366     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-17534-3_81     Document Type: Conference Paper

References (18)

1. Mika, S., Ratsch, G., Weston, J., Schölkopf, B., Mullers, K.R.: Fisher Discriminant Analysis with Kernels. In: IEEE Conference on Neural Networks for Signal Processing IX, pp. 41-48 (1999)
2. Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
3. Dadgostar, F., Sarrafzadeh, A., Messom, C.H.: Multi-layered Hand and Face Tracking for Real-Time Gesture Recognition. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008. LNCS, vol. 5506, pp. 587-594. Springer, Heidelberg (2009)
4. Mika, S., Smola, A., Schölkopf, B.: An Improved Training Algorithm for Kernel Fisher Discriminants. In: AISTATS, pp. 98-104 (2001)
5. Diethe, T., Hussain, Z., Hardoon, D.R., Shawe-Taylor, J.: Matching Pursuit Kernel Fisher Discriminant Analysis. In: International Conference on Artificial Intelligence and Statistics, pp. 121-128 (2009)
6. Xing, H.-J., Yang, Y.-J., Wang, Y., Hu, B.-G.: Sparse Kernel Fisher Discriminant Analysis. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 824-830. Springer, Heidelberg (2005)
7. Burges, C.J.C.: Simplified Support Vector Decision Rules. In: 13th International Conference on Machine Learning, pp. 71-77 (1996)
8. Schölkopf, B., Knirsch, P., Smola, A., Burges, C.J.C.: Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces. In: DAGM Symposium Mustererkennung. LNCS, pp. 124-132. Springer, Heidelberg (1998)
9. Mika, S., Schölkopf, B., Smola, A., Müller, K.R., Scholz, M., Rätsc, G.: Kernel PCA and De-noising in Feature Spaces. In: Kearns, M.S., Solla, S.A., Cohn, D.A. (eds.) Advances in Neural Information Processing Systems, vol. 11, pp. 536-542. Morgan Kaufmann, San Mateo (1998)
10. Kwok, J.T., Tsang, I.W.: The Pre-Image Problem in Kernel Methods. IEEE Transactions on Neural Networks 15(6), 1517-1525 (2004)
11. Wang, L., Bo, L., Jiao, L.: Kernel Uncorrelated Discriminant Analysis for Radar Target Recognition. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4233, pp. 404-411. Springer, Heidelberg (2006)
12. Girosi, F., Jones, M., Poggio, T.: Regularization Theory and Neural Network Architectures. Neural Computation 7, 219-269 (1995)
13. Honeine, P., Richard, C.: A Closed-form Solution for the Pre-image Problem in Kernelbased Machines. Journal of Signal Processing Systems, 1939-8018 (2010)
14. Teixeira, A.R., Tomé, A.M., Stadlthanner, K., Lang, E.W.: KPCA Denoising and the Preimage Problem Revisited. Digital Signal Processing 18(4), 568-580 (2008)
15. Shin, H., Cho, S.: Invariance of Neighborhood Relation under Input Space to Feature Space Mapping. Pattern Recognition Letters 26(6), 707-718 (2005)
16. Scholkopf, B., Mika, S., Burges, C.J.C., Knirsch, P., Muller, K.-R., Ratsch, G., Smola, A.J.: Input Space Versus Feature Space in Kernel-based Methods. IEEE Transactions on Neural Networks 10(5), 1000-1017 (1999)
17. Vincent, P., Bengio, Y.: Kernel Matching Pursuit. Machine Learning 48, 165-187 (2002)
18. Blake, C.L., Merz, C.J.: UCI Repostitory of Machine Learning Databases. Department of Information and Computer Science. University of California, Irvine (1998), http://www.ics.uci.edu/.mlearn/MLRepository.html