Grouping and dimensionality reduction by locally linear embedding

被引：0
|
作者：
Polito, M [1 ]
Perona, P [1 ]
机构：
[1] CALTECH, Div Phys Math & Astron, Pasadena, CA 91125 USA
来源：
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 14, VOLS 1 AND 2 | 2002年 / 14卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Locally Linear Embedding (LLE) is an elegant nonlinear dimensionality-reduction technique recently introduced by Roweis and Saul [2]. It fails when the data is divided into separate groups. We study a variant of LLE that can simultaneously group the data and calculate local embedding of each group. An estimate for the upper bound on the intrinsic dimension of the data set is obtained automatically.
引用
收藏
页码：1255 / 1262
页数：8
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