Computing Large-Scale Matrix and Tensor Decomposition With Structured Factors: A Unified Nonconvex Optimization Perspective
Computing Large-Scale Matrix and Tensor Decomposition With Structured Factors: A Unified Nonconvex Optimization Perspective
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DOI：
10.1109/msp.2020.3003544
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发表时间：
2020-06
影响因子：
14.9
通讯作者：
Xiao Fu;Nico Vervliet;L. De Lathauwer;Kejun Huang;Nicolas Gillis
中科院分区：
文献类型：
--
作者：
Xiao Fu;Nico Vervliet;L. De Lathauwer;Kejun Huang;Nicolas Gillis
在过去的20年中，低级张量和矩阵分解模型（LRDMS）已成为信号处理，机器学习和数据科学的必不可少的工具。 LRDMS使用简洁和简约的低维潜在因素代表高维，多样化和多模式数据。 LRDM可以提供多种目的，例如数据嵌入（降低维度），去核，潜在变量分析，模型参数估计和大数据压缩；有关应用程序的调查，请参见[1] - [5]。
During the past 20 years, low-rank tensor and matrix decomposition models (LRDMs) have become indispensable tools for signal processing, machine learning, and data science. LRDMs represent high-dimensional, multiaspect, and multimodal data using low-dimensional latent factors in a succinct and parsimonious way. LRDMs can serve a variety of purposes, e.g., data embedding (dimensionality reduction), denoising, latent variable analysis, model parameter estimation, and big data compression; see [1]-[5] for surveys of applications.