Journal
KNOWLEDGE-BASED SYSTEMS
Volume 21, Issue 7, Pages 535-539Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2008.03.014
Keywords
Two-dimensional singular value decomposition; Multivariate time series; Classification
Categories
Funding
- National Science Foundation of China [60173058]
Ask authors/readers for more resources
Multivariate time series (MTS) are used in very broad areas such as multimedia, medicine, finance and speech recognition. A new approach for MTS classification using two-dimensional singular value decomposition (2dSVD) is proposed. 2dSVD is an extension of standard SVD, it captures explicitly the two-dimensional nature of MTS samples. The eigenvectors of row-row and column-column covariance matrices of MTS samples are computed for feature extraction. After the feature matrix is obtained for each MTS sample, one-nearest-neighbor classifier is used for MTS classification. Experimental results performed on five real-world datasets demonstrate the effectiveness of our proposed approach. (c) 2008 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available