Self-Regulation of Neighborhood Parameter for Locally Linear Embedding

Kang Hua Hui; Chun Li Li; Xin Zhong Xu; Xiao Rong Feng

doi:10.4028/www.scientific.net/AMR.677.436

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 677Self-Regulation of Neighborhood Parameter for...

Self-Regulation of Neighborhood Parameter for Locally Linear Embedding

Abstract:

The locally linear embedding (LLE) algorithm is considered as a powerful method for the problem of nonlinear dimensionality reduction. In this paper, a new method called Self-Regulated LLE is proposed. It achieves to solve the problem of deciding appropriate neighborhood parameter for LLE by finding the local patch which is close to be a linear one. The experiment results show that LLE with self-regulation performs better in most cases than LLE based on different evaluation criteria and spends less time on several data sets.

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Periodical:

Advanced Materials Research (Volume 677)

Pages:

436-441

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.677.436

Citation:

Cite this paper

Online since:

March 2013

Authors:

Kang Hua Hui, Chun Li Li, Xin Zhong Xu, Xiao Rong Feng

Keywords:

Dimensionality Reduction, Locally Linear Embedding (LLE), Manifold Learning, Self-Regulation

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