Output details
11 - Computer Science and Informatics
Kingston University
Article title
Structural Laplacian Eigenmaps for Modeling Sets of Multivariate Sequences
Type
D - Journal article
Title of journal
IEEE Transactions on Cybernetics
Article number
-
Volume number
PP
Issue number
99
First page of article
n/a
ISSN of journal
2168-2275
Year of publication
2013
Number of additional authors
3
Additional information
<24> This presents a novel dimensionality reduction method (Structural Laplacian Eigenmaps) that explicitly deals with the problem of dimensionality reduction of time series and clearly outperforms any previous approaches. Results are presented on many datasets of high-dimensional time series, such as 3D motion capture sequences and view-variant sets of object images. The preceding conference paper’s (ICPR2010) high citations suggest there is a significant interest in this research.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
-
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-