Kernel Scatter-difference Based Discriminant Locality Preserving Projection for Image Recognition ?
نویسندگان
چکیده
Locality preserving projection (LPP) aims at finding an embedded subspace that preserves the local structure of data. Though LPP can provide intrinsic compact representation for image data, it has limitations on image recognition. In this paper, an improved algorithm called kernel scatter-difference based discriminant locality preserving projection (KSDLPP) is proposed. KSDLPP uses kernel trick method to map the input data into an implicit feature space where a scatter-difference discriminant rule based LPP is employed to seek a low-dimensional manifold subspace. Not only does KSDLPP describe complex nonlinear structure of the images, but it also avoids the singularity problem of high-dimensional data matrix and offers better classification capability. Experiment results on public face and palmprint databases also demonstrate the effective recognition performance of the KSDLPP algorithm.
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