Chunk-based Distributed Tensor-Train Decomposition Methods for Cyber-Physical-Social Intelligence

Cyber-Physical-Social Systems (CPSS), emerging as a new paradigm of computing applications, combine CyberPhysical Systems (CPS) with the social characteristics for the Cyber-Physical-Social Intelligence to provide the services at the right time, the right place and by the right means. For this purpose, CPSS Big Data should be processed and analyzed efficiently to explore the hidden information of users. As an effective processing tool of CPSS Big Data from the perspective of multi-attributes, tensor with its decomposition methods has attracted much attention. At the same time, tensor-train (TT), a potential tensor decomposition method, has proven to be an efficient CPSS data analysis method. In this paper, as the extension of our previous work, chunk-based distributed tensor-train decomposition methods with the tensor divided in the form of blocks are proposed to speed up data processing and improve the adaptability and extensibility. Finally, experiments carried out on the random data and medical image data are used to measure the performance of the proposed methods including the error and computational efficiency