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2014 A Kernel Based Neighborhood Discriminant Submanifold Learning for Pattern Classification
Xu Zhao
J. Appl. Math. 2014: 1-10 (2014). DOI: 10.1155/2014/950349

Abstract

We propose a novel method, called Kernel Neighborhood Discriminant Analysis (KNDA), which can be regarded as a supervised kernel extension of Locality Preserving Projection (LPP). KNDA nonlinearly maps the original data into a kernel space in which two graphs are constructed to depict the within-class submanifold and the between-class submanifold. Then a criterion function which minimizes the quotient between the within-class representation and the between-class representation of the submanifolds is designed to separate each submanifold constructed by each class. The real contribution of this paper is that we bring and extend the submanifold based algorithm to a general model and by some derivation a simple result is given by which we can classify a given object to a predefined class effectively. Experiments on the MNIST Handwritten Digits database, the Binary Alphadigits database, the ORL face database, the Extended Yale Face Database B, and a downloaded documents dataset demonstrate the effectiveness and robustness of the proposed method.

Citation

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Xu Zhao. "A Kernel Based Neighborhood Discriminant Submanifold Learning for Pattern Classification." J. Appl. Math. 2014 1 - 10, 2014. https://doi.org/10.1155/2014/950349

Information

Published: 2014
First available in Project Euclid: 2 March 2015

MathSciNet: MR3166797
Digital Object Identifier: 10.1155/2014/950349

Rights: Copyright © 2014 Hindawi

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