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| A Kernel Based Supervised Manifold Learning Algorithm |
| LI Jun-Bao1, PAN Jeng-Shyang2 |
1.Institute of Automatic Test and Control, Harbin Institute of Technology, Harbin 1500012.
Department of Electronic Engineering, Kaohsiung University of Applied Sciences, Kaohsiung China |
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Abstract A kernel based supervised manifold learning method is presented to solve the problems on parameter selection with locality preserving projection and inability in nonlinear feature extraction, which is unresolved by the currently proposed manifold learning algorithm. The proposed algorithm is an improvement of locality preserving projection (LPP). The nearest neighbor graph is created with the class label information of the samples, and nonparametric similarity measure is used. The kernel method is used to solve the limitation problem of the nonlinear separability for LPP. The feasibility and effectivity of the proposed algorithm are testified on two databases.
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Received: 15 January 2007
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2008, Vol. 21 
