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

doi:10.23919/JSEE.2020.000052

Abstract

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

Ke ZHANG, Le CUI, Yao YIN. A multivariate grey incidence model for different scale data based on spatial pyramid pooling[J]. Journal of Systems Engineering and Electronics, 2020, 31(4): 770-779.

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$F(c)$	Output size of SPP
	output_$m$=3			output_$m$=3			output_$m$=4			output_$m$=4			output_$m$=5			output_$m$=5
	output_$n$=6			output_$n$=12			output_$n$=6			output_$n$=12			output_$n$=6			output_$n$=12
	$k$=1	$k$=5	$k$=10	$k$=1	$k$=5	$k$=10	$k$=1	$k$=5	$k$=10	$k$=1	$k$=5	$k$=10	$k$=1	$k$=5	$k$=10	$k$=1	$k$=5	$k$=10
$F(C)=\min(C)$	0.79	0.74	0.7	0.94	0.89	0.83	0.79	0.73	0.69	0.98	0.91	0.85	0.8	0.71	0.65	0.94	0.88	0.82
$F(C)=\max(C)$	0.87	0.79	0.73	0.94	0.87	0.8	0.85	0.81	0.76	0.93	0.9	0.84	0.86	0.79	0.72	0.94	0.87	0.81
$F(C)={\rm ave}(C)$	0.91	0.85	0.81	0.97	0.91	0.87	0.9	0.84	0.79	0.94	0.89	0.85	0.9	0.84	0.77	0.94	0.88	0.82

Parameter $k$	MGIM-SPP	PD	DTW	PCA+DTW
1	0.966 7	0.611 1	0.948 1	0.285 2
5	0.912 5	0.560 0	0.905 2	0.243 0
10	0.869 2	0.511 5	0.866 7	0.213 0

Parameter $k$	MGIM-SPP	DTW	PD	PCA+DTW
1	0.949 1	0.976 9	0.717 6	0.828 7
5	0.872 2	0.906 5	0.674 1	0.749 1
10	0.835 1	0.916 7	0.630 6	0.713 4

Dataset	DTW	PD	SPP-MGIM	PCA+DTW
JV	957	1	1	408
ASL	7 240	2	2	3 188

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