Paper Info
Title
An Improved LMDS Algorithm.
Abstract
Classical multidimensional scaling (CMDS) is a widely used method for dimensionality reduction and data visualization, but it's very slow. Landmark MDS (LMDS) is a fast algorithm of CMDS. In LMDS, some data points are designated as landmark points. When the intrinsic dimension of the landmark points is less than the intrinsic dimension of the data set, the embedding recovered by LMDS is not consistent with that of classical multidimensional scaling. A selection algorithm of landmark points is put forward in this paper to ensure the intrinsic dimension of the landmarks to be equal to that of the data set, so as to ensure that the embedding recovered by LMDS is the same as that of CMDS. By introducing the selection algorithm into the original LMDS, an improved LMDS algorithm called iLMDS is presented in this paper. The experimental results verify the consistency of iLMDS and CMDS.
Year
DOI
Venue
2016
10.1007/978-3-319-41009-8_10
ADVANCES IN SWARM INTELLIGENCE, ICSI 2016, PT II
Keywords
Field
DocType
Classical multidimensional scaling,Landmark points,Intrinsic dimension,Selection algorithm
Data point,Local Multipoint Distribution Service,Data visualization,Dimensionality reduction,Embedding,Multidimensional scaling,Computer science,Selection algorithm,Algorithm,Intrinsic dimension
Conference
Volume
ISSN
Citations 
9713
0302-9743
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Taiguo Qu111.37
Zixing Cai2152566.96