TS-RTPM-Net: Data-Driven Tensor Sketching for Efficient CP Decomposition

Abstract

Tensor decomposition is widely used in feature extraction, data analysis, and other fields. As a means of tensor decomposition, the robust tensor power method based on tensor sketch (TS-RTPM) can quickly mine the potential features of tensor, but in some cases, its approximation performance is limited. In this paper, we propose a data-driven framework called TS-RTPM-Net, which improves the estimation accuracy of TS-RTPM by jointly training the TS value matrices with the RTPM initial matrices. It also uses two greedy initialization algorithms to optimize the TS location matrices. In addition, TS-RTPM-Net accelerates TS-RTPM by using fast power iteration modules. Comparative experiments on real-world datasets verify that TS-RTPM-Net outperforms TS-RTPM in terms of estimation accuracy, running speed, and memory consumption.