一种基于扩散映射的非线性降维算法

doi:10.3969/j.issn.1001-2400.2010.01.023

J4 ›› 2010, Vol. 37 ›› Issue (1): 130-135.doi: 10.3969/j.issn.1001-2400.2010.01.023

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Nonlinear dimensionality reduction of manifolds by diffusion maps

SHANG Xiao-qing;SONG Yi-mei

(School of Science, Xidian Univ., Xi'an 710071， China)

Received:2009-01-02 Online:2010-02-20 Published:2010-03-29
Contact: SHANG Xiao-qing E-mail:xqshang@xidian.edu.cn

Abstract

Abstract:

Nonlinear dimensionality reduction programs keep the local properties but relax the distances between points which are not in a neighborhood. As a new learning framework, the diffusion method realizes dimensionality reduction in a diffusion processing. Based on the theory of diffusion maps, this paper discusses the numerical method for spectral decomposition and presents the diffusion maps algorithm(DMA). Experimental results show that the DMA technique can detect the intrinsic dimensionality in high-dimensional data and is more stable in noise case.

Key words: diffusion maps, manifold learning, nonlinear dimensionality reduction

SHANG Xiao-qing;SONG Yi-mei. Nonlinear dimensionality reduction of manifolds by diffusion maps[J].J4, 2010, 37(1): 130-135.

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Nonlinear dimensionality reduction of manifolds by diffusion maps

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