Abstract
In this paper, a set of hybrid dimension reduction schemes is constructed by unifying principal component analysis (PCA) and linear discriminant analysis (LDA) in a single framework. PCA compensates LDA for singular scatter matrix caused by small set of training samples and increases the effective dimension of the projected subspace. Generalization of hybrid analysis is extended to other discriminant analysis such as multiple discriminant analysis (MDA), and the recent biased discriminant analysis (BDA), and other hybrid pairs. In order to reduce the search time to find the best single classifier, a boosted hybrid analysis is proposed. Our scheme boosts both the individual features as well as a set of weak classifiers. Extensive tests on benchmark and real image databases have shown the superior performance of the boosted hybrid analysis.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Jolliffe, T.: Principal Component Analysis, 2nd edn. Springer, New York (2002)
Torgerson, W.S.: Psychometrika. 17, 401–419 (1952)
Tenenbaum, J., Silva, V.D., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)
Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley & Sons, Inc., Chichester (2001)
Martinez, M., Kak, A.C.: PCA versus LDA. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)
Zhou, X., Huang, T.S.: Small sample learning during multimedia retrieval using biasMap. In: Proc. of IEEE Conf. Computer Vision and Pattern Recognition (December 2001)
Friedman, J.: Regularized discriminant analysis. Journal of American Statistical Association 84(405), 165–175 (1989)
Tian, Q., Yu, J., Rui, T., Huang, T.S.: Parameterized discriminant analysis for image classification. In: Proc. IEEE Int’l Conf. on Multimedia and Expo., Taiwan (2004)
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.: Fisher discriminant analysis with kernels. In: IEEE Workshop on Neural Networks for Signal Proc. 1999 (1999)
Tian, Q., Wu, Y., Yu, J., Huang, T.S.: Self-supervised learning based on discriminative nonlinear features for image classification. Pattern Recognition 38(6) (2005)
Freund, Y.: Boosting a weak learning algorithm by majority. Information and Computation 121(2), 256–285 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tian, Q., Yu, J., Huang, T.S. (2005). Boosting Multiple Classifiers Constructed by Hybrid Discriminant Analysis. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_5
Download citation
DOI: https://doi.org/10.1007/11494683_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26306-7
Online ISBN: 978-3-540-31578-0
eBook Packages: Computer ScienceComputer Science (R0)