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

delete2024-02-01
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PRE
AI
X
Xingyu Cao
X
Xiangtao Zhang
C
Ce Zhu
J
Jiani Liu
Y
Yipeng Liu *
DOI：10.1109/TBDATA.2023.3310254delete
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Abstract

Abstract

En 中文
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.
Keywords:
Accelerated tensor decomposition
data-driven algorithms
dimensionality reduction
greedy algorithms
low-rank tensor approximation

Journal

I
IEEE Transactions on Big Data
IF:
5.7
Papers:
834
Citations:
3.0K

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