面向大数据处理的快速张量分解

V. Nguyen, K. Abed-Meraim, N. Linh-Trung
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引用次数: 7

摘要

张量作为矩阵的自然扩展,其分解为心理测量学、信号处理、数据通信、计算机视觉和机器学习等许多学科提供了重要的工具。本文的主要目的是简要回顾最近的几种最先进的大规模张量数据方法,这是大数据的重要组成部分。此外,我们还介绍了自己在这个主题上的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast tensor decompositions for big data processing
Tensors, as a natural extension of matrices, and their decompositions provide important tools in many disciplines such as psychometrics, signal processing, data communication, computer vision, and machine learning. The main objective of this paper is to briefly review several recent state-of-the-art approaches for large-scale tensor data which is a crucial part of big data. Moreover, we also introduce our own contributions on this topic.
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