期刊
NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS
卷 13, 期 2-3, 页码 149-171出版社
WILEY
DOI: 10.1002/nla.475
关键词
multi-rid; multiresolution; multidimensional scaling; isometric embedding; SMACOF; BFGS; face recoanition; bending-invariant canonical form; dimensionality reduction
Multidimensional scaling (MDS) is a generic name for a family of algorithms that construct a configuration of points in a target metric space from information about inter-point distances measured in some other metric space. Large-scale MDS problems often occur in data analysis, representation and visualization. Solving such problems efficiently is of key importance in many applications. In this paper we present a multi.-rid framework for MDS problems. We demonstrate the performance of our algorithm on dimensionality reduction and isometric embedding problems, two classical problems requiring efficient large-scale MDS. Simulation results show that the proposed approach significantly outperforms conventional MDS algorithms. Copyright (c) 2006 John Wiley & Sons, Ltd.
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