Quantization and application of low-rank tensor decomposition based on the deep learning model.

Abstract

Watching the presentation of a large-scale network is very important for network state tracking, performance optimization, traffic engineering, anomaly detection, fault analysis, etc. In this paper, we try to develop deep learning technology to solve the defect problem of tensor filling based on inner product interaction. To solve the limitations of the existing tensor-filling algorithms, a new neural tensor-filling (NTC) model is proposed. NTC model can effectively type the third-order communication between data landscapes through outer creation operation. It creates the third-order interaction mapping tensor. On this basis, the interaction between local features of the 3D neural network is studied. In this paper, another fusion neural tensor filling (Fu NTC) model is proposed to solve the problem that the NTC model can only extract the nonlinear complex structural information between potential feature dimensions. In the framework of the neural network, the NTC model and tensor decomposition model share the same potential feature embedding. It can effectively extract nonlinear feature information and linear feature information at the same time. It achieves higher precision data recovery.