Evolving insight into high-dimensional data

Tu, Yiqing, Li, Gang and Dai, Honghua 2005, Evolving insight into high-dimensional data, Lecture notes in computer science, vol. 3644, pp. 465-474, doi: 10.1007/11538059_49.

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Title Evolving insight into high-dimensional data
Author(s) Tu, Yiqing
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Dai, HonghuaORCID iD for Dai, Honghua orcid.org/0000-0001-9899-7029
Journal name Lecture notes in computer science
Volume number 3644
Start page 465
End page 474
Publisher Springer-Verlag
Place of publication Heidelberg, Germany
Publication date 2005
ISSN 0302-9743
Summary ISOMap is a popular method for nonlinear dimensionality reduction in batch mode, but need to run its entirety inefficiently if the data comes sequentially. In this paper, we present an extension of ISOMap, namely I-ISOMap, augmenting the existing ISOMap framework to the situation where additional points become available after initial manifold is constructed. The MDS step, as a key component in ISOMap, is adapted by introducing Spring model and sampling strategy. As a result, it consumes only linear time to obtain a stable layout due to the Spring model’s iterative nature. The proposed method outperforms earlier work by Law [1], where their MDS step runs within quadratic time. Experimental results show that I-ISOMap is a precise and efficient technique for capturing evolving manifold.
Notes Book Title: Advances in intelligent computing
Language eng
DOI 10.1007/11538059_49
Field of Research 080299 Computation Theory and Mathematics not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2005, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30003065

Document type: Journal Article
Collection: School of Information Technology
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