Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (4): 770-779.doi: 10.23919/JSEE.2020.000052

• Systems Engineering • Previous Articles     Next Articles

A multivariate grey incidence model for different scale data based on spatial pyramid pooling

Ke ZHANG*(), Le CUI(), Yao YIN()   

  • Received:2019-07-16 Online:2020-08-25 Published:2020-08-25
  • Contact: Ke ZHANG E-mail:kezhang@hhu.edu.cn;cuile_19@foxmail.com;18260061373@163.com
  • About author:ZHANG Ke was born in 1983. He received both hisB.S. and M.S. degrees in electronic information engineering fromNanchang Hangkong University in 2004 and 2007 respectively, andPh.D. degree in system engineering from Nanjing University ofAeronautics and Astronautics, Nanjing, China. Currently, he is anassociate professor at the Business School, Hohai University, China. His research interestsinclude grey system theory anduncertainty system modeling.E-mail: kezhang@hhu.edu.cn|CUI Le was born in 1997. She received her B.S. degree in information management and information system from Hohai University in 2018. Currently, she is a postgraduate in Hohai University. Her research interests are data mining and machine learning. E-mail: cuile_19@foxmail.com|YIN Yao was born in 1995. He received his B.S. degree in information management and information system from Hohai University in 2017. He is a postgraduate in Hohai University. His research interest is machine learning. E-mail: 18260061373@163.com
  • Supported by:
    the National Natural Science Foundation of China(71401052);the Fundamental Research Funds for the Central Universities(2019B19514);This work was supported by the National Natural Science Foundation of China (71401052) and the Fundamental Research Funds for the Central Universities (2019B19514)

Abstract:

In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling. Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct $n$ levels feature pooling matrices on the same scale. Secondly, Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.

Key words: grey system, spatial pyramid pooling, grey incidence, multivariate time series