基于交替最小二乘法的时空张量填充算法

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Abstract In the network system, data missing cannot be avoided regardless of the traffic measurement systems. To solve the problem of missing network traffic data, we propose a spatio-temporal tensor completion algorithm based on alternating least squares (ALS) to recover the missing values in the traffic data tensor. The proposed algorithm not only utilizes tensor decomposition and its low-dimensional representation, but also fully considers the spatio-temporal correlation of network traffic data, and further improves the accuracy of data recovery. This paper uses the Abilene dataset to test the algorithm and compares it to the state-of-the-art completion methods. The experimental results show that the proposed method can effectively reduce the error of traffic data recovery and improve the accuracy of data recovery.

Key words： alternating least squares (ALS) data missing tensor completion spatio-temporal constraint

Received: 13 May 2019 Published: 19 December 2019

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	Wang Can
	Feng Xinxin

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Wang Can,Feng Xinxin. Spatio-temporal tensor completion algorithm based on alternating least squares[J]. Electrical Engineering, 2019, 20(12): 35-40.

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http://dqjs.cesmedia.cn/EN/Y2019/V20/I12/35

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