基于模糊聚类和多维标度方法的非线性成分分析

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI:10.1109/IJCNN.1999.833473

Eriko Ikeda, T. Imaoka, H. Ichihashi, T. Miyoshi

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引用次数: 1

摘要

针对多维数据集的降维和表示问题，提出了一种新的非线性分量分析策略。该方法包括两个步骤:一是根据两点之间的局部距离将数据集划分为多个聚类，二是通过多维尺度方法将得到的子流形投影到低维线性空间上。

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Nonlinear component analysis by fuzzy clustering and multidimensional scaling methods

This paper proposes a new strategy of nonlinear component analysis for dimensionality reduction and representation of multidimensional data sets. The proposed procedure consists of two steps: one is to partition the data set into several clusters based on the local distances between two points, and the other is to project the obtained sub-manifolds on a low dimensional linear space by the multidimensional scaling methods.

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IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)

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