Kernel relative transformation with applications to enhancing locally linear embedding

Abstract

Locally linear embedding heavily depends on whether the neighborhood graph represents the underlying geometry structure of the data manifolds. Inspired from the cognitive law, the relative transformation(RT) and kernel relative transformation (KRT) are proposed. They can improve the distinction between data points and inhibit the impact of noise and… (More)
DOI: 10.1109/IJCNN.2008.4634281

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Cite this paper

@article{Wen2008KernelRT, title={Kernel relative transformation with applications to enhancing locally linear embedding}, author={Guihua Wen and Lijun Jiang and Jun Wen}, journal={2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)}, year={2008}, pages={3401-3406} }