Classification Algorithm Based on Stationary Subspace Analysis and Relative Entropy
Journal Title: 河南科技大学学报（自然科学版） - Year 2016, Vol 37, Issue 6
Abstract
Based on the characteristics that the multivariate time series were multivariate and had complex relationship among the variables,a classification algorithm based on stationary subspace analysis( SSA) and relative entropy( KL) was proposed. Firstly,stationary subspace analysis method was adopted to divide the multivariate data into the stationary subspace and the non-stationary subspace. Then,relative entropy was used to measure the similarity of distributions between the stationary subspaces. Finally,the classification of real dataset was carried out. The results show that the SSA-KL algorithm is better than the DTW algorithm and the PCA-ED algorithm.
Authors and Affiliations
Xiaoyuan ZHOU, Jinghu YU
Keywords
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- EP ID EP477474
- DOI 10.15926/j.cnki.issn1672-6871.2016.06.009
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