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11 - Computer Science and Informatics

Kingston University

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Article title

Structural Laplacian Eigenmaps for Modeling Sets of Multivariate Sequences

Type

D - Journal article

DOI

10.1109/TCYB.2013.2277664

Title of journal

IEEE Transactions on Cybernetics

Article number

Volume number

Issue number

First page of article

n/a

ISSN of journal

2168-2275

Year of publication

2013

URL

http://eprints.kingston.ac.uk/id/eprint/26697

Number of additional authors

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

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract