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- Title
Multivariate time series classification using kernel matrix.
- Authors
Jiancheng Sun; Huimin Niu; Zongqing Tu; Zhinan Wu; Si Chen
- Abstract
Multivariate time series (MTS) classification is a fundamental problem in time series mining, and the approach based on covariance matrix is an attractive way to solve the classification. In this study, it is noted that a traditional covariance matrix is only a particular form of kernel matrices, and then presented a classification method for MTS. First, the Gaussian kernel matrix is employed to replace the traditional covariance matrix. Then the kernel matrix is mapped into the tangent space of Riemannian manifold. Finally, the classification is implemented by choosing a classification algorithm. The experimental results show that the classification performance based on the Gaussian kernel matrix outperforms the other two methods, which indicates that an appropriate kernel function is an essential factor in improving the classification performance.
- Subjects
RIEMANNIAN manifolds; COVARIANCE matrices; CLASSIFICATION algorithms; CLASSIFICATION; MATRICES (Mathematics)
- Publication
Electronics Letters (Wiley-Blackwell), 2022, Vol 58, Issue 7, p293
- ISSN
0013-5194
- Publication type
Article
- DOI
10.1049/ell2.12442