Abstract
Local Preserving Projection (LPP) is an unsupervised feature extraction method which considers the nearest neighbor information and has little to do with the class information, and it fails to perform well for the nonlinear problems due to its limitation of linearity. In this paper, we extend LPP to propose a novel feature extraction namely Kernel Locally Discriminant Projection (KLDP) by considering class label information and the nonlinear problems. The main work lies in: 1) the class label information is considered to create the similarity measure for the local structure graph; 2) the class-wise cosine similarity measure is applied to solve the selection of the free parameter of the similarity measure; 3) kernel method is applied to solve limitation of linearity of LPP. Besides some theory derivation, the experiments are implemented on ORL and Yale face database to evaluate the feasibility of the proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290 (December 2000)
Samaria, F., Harter, A.: Parameterisation of a Stochastic Model for Human Face Identification. In: Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, December 1994 (1994)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Lu, J.W., Plataniotis, K., Venetsanopoulos, A.N.: Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans. Neural Network 14(1), 117–126 (2003)
Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: A kernel machine based approach for multi-view face recognition. In: Proc. Int. Conf. Image Processing, I-265–I-268 (2002)
He, X., Niyogi, P.: Locality Preserving Projections. In: Proc. Conf. Advances in Neural Information Processing Systems (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Li, JB., Chu, SC., Pan, JS. (2007). Locally Discriminant Projection with Kernels for Feature Extraction. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_56
Download citation
DOI: https://doi.org/10.1007/978-3-540-73871-8_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73870-1
Online ISBN: 978-3-540-73871-8
eBook Packages: Computer ScienceComputer Science (R0)