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Indexed by:期刊论文

Date of Publication:2018-07-01

Journal:IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

Included Journals:SCIE

Volume:14

Issue:7

Page Number:3197-3204

ISSN No.:1551-3203

Key Words:Big data; deep computation; industry informatics; tensor-train network

Abstract:The deep computation model has been proved to be effective for big data hierarchical feature and representation learning in the tensor space. However, it requires expensively computational resources including high-performance computing units and large memory to train a deep computation model with a large number of parameters, limiting its effectiveness and efficiency for industry informatics big data feature learning. In this paper, a tensor-train deep computation model is presented for industry informatics big data feature learning. Specially, the tensor-train network is used to compress the parameters significantly by converting the dense weight tensors into the tensor-train format. Furthermore, a learning algorithm is implemented based on gradient descent and back-propagation to train the parameters of the presented tensor-train deep computation model. Extensive experiments are carried on STL-10, CUAVE, and SNAE2 to evaluate the presented model in terms of the approximation error, classification accuracy drop, parameters reduction, and speedup. Results demonstrate that the presented model can improve the training efficiency and save the memory space greatly for the deep computation model with small accuracy drops, proving its potential for industry informatics big data feature learning.