Simulation Research of Feature Extraction Method Based on Nonlinear Manifold Learning

Abstract:

This paper deals with the study of Locally Linear Embedding (LLE) and Hessian LLE nonlinear feature extraction for high dimensional data dimension reduction. LLE and Hessian LLE algorithm which reveals the characteristics of nonlinear manifold learning were analyzed. LLE and Hessian LLE algorithm simulation research was studied through different kinds of sample for dimensionality reduction. LLE and Hessian LLE algorithm’s classification performance was compared in accordance with MDS. The simulation experimental results show that LLE and Hessian LLE are very effective feature extraction method for nonlinear manifold learning.

• Periodical: Applied Mechanics and Materials (Volume 533)
• Pages: 247-251
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.533.247
• Online since: February 2014
• Authors: Hai Bing Xiao*, Xiao Peng Xie
• Keywords: Feature Extraction, Hessian LLE, Locally Linear Embedding, Nonlinear Manifold

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