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政大機構典藏 > 商學院 > 統計學系 > 期刊論文 > Item 140.119/139844

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/139844

Title:	Isometric sliced inverse regression for nonlinear manifolds learning
Authors:	吳漢銘 Wu, Han-Ming Yao, Wei-Ting
Contributors:	統計系
Keywords:	K-means clustering;Isometric feature mapping (ISOMAP);Nonlinear dimension reduction;Nonlinear manifold;Rank-two ellipse seriation;Sliced inverse regression
Date:	2013-09
Issue Date:	2022-04-12
Abstract:	Sliced inverse regression (SIR) was developed to find effective linear dimension-reduction directions for exploring the intrinsic structure of the high-dimensional data. In this study, we present isometric SIR for nonlinear dimension reduction, which is a hybrid of the SIR method using the geodesic distance approximation. First, the proposed method computes the isometric distance between data points; the resulting distance matrix is then sliced according to K-means clustering results, and the classical SIR algorithm is applied. We show that the isometric SIR (ISOSIR) can reveal the geometric structure of a nonlinear manifold dataset (e.g., the Swiss roll). We report and discuss this novel method in comparison to several existing dimension-reduction techniques for data visualization and classification problems. The results show that ISOSIR is a promising nonlinear feature extractor for classification applications.
Relation:	Statistics & Computing, Vol.23, No. 5, pp.563-576
Data Type:	article
DOI 連結:	https://doi.org/10.1007/s11222-012-9330-z
DOI:	10.1007/s11222-012-9330-z
Appears in Collections:	[統計學系] 期刊論文

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