Neriman Tokcan , Shakir Showkat Sofi , Van Tien Pham , Clémence Prévost , Sofiane Kharbech , Baptiste Magnier , Thanh Phuong Nguyen , Yassine Zniyed , Lieven De Lathauwer
{"title":"Tensor decompositions for signal processing: Theory, advances, and applications","authors":"Neriman Tokcan , Shakir Showkat Sofi , Van Tien Pham , Clémence Prévost , Sofiane Kharbech , Baptiste Magnier , Thanh Phuong Nguyen , Yassine Zniyed , Lieven De Lathauwer","doi":"10.1016/j.sigpro.2025.110191","DOIUrl":null,"url":null,"abstract":"<div><div>In the era of big data, rapid advancements in technology and data collection methods have led to the generation and accessibility of vast amounts of multi-modal, high-dimensional data across a diverse range of disciplines. Tensor methods have emerged as essential tools in signal processing, providing powerful frameworks to model and analyze such complex data effectively. This survey offers a comprehensive overview of tensor factorization techniques and their applications in key areas. We explore their role in remote sensing, focusing on tensor-based methods for analyzing hyperspectral and multispectral images, tackling challenges such as recovering super-resolution images and addressing spectral unmixing. In wireless communication, we examine tensor methods used for signal modulation in unsourced massive random access communication, which achieve strong performance in multi-antenna channel and signal modeling. We also discuss tensor applications in network compression, where they reduce the computational demands of deep neural networks, making them more feasible for edge devices. Additionally, we highlight the use of tensor methods in high-dimensional missing data completion problems, showcasing their versatility across various domains. Furthermore, we explore applications in image analysis and computer vision, where tensors are effectively utilized for motion and object tracking, 3D modeling, satellite image analysis, and medical imaging. By bridging theoretical advancements with practical applications, this survey aims to guide researchers in leveraging tensor methods to tackle emerging challenges in signal processing.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110191"},"PeriodicalIF":3.6000,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165168425003056","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In the era of big data, rapid advancements in technology and data collection methods have led to the generation and accessibility of vast amounts of multi-modal, high-dimensional data across a diverse range of disciplines. Tensor methods have emerged as essential tools in signal processing, providing powerful frameworks to model and analyze such complex data effectively. This survey offers a comprehensive overview of tensor factorization techniques and their applications in key areas. We explore their role in remote sensing, focusing on tensor-based methods for analyzing hyperspectral and multispectral images, tackling challenges such as recovering super-resolution images and addressing spectral unmixing. In wireless communication, we examine tensor methods used for signal modulation in unsourced massive random access communication, which achieve strong performance in multi-antenna channel and signal modeling. We also discuss tensor applications in network compression, where they reduce the computational demands of deep neural networks, making them more feasible for edge devices. Additionally, we highlight the use of tensor methods in high-dimensional missing data completion problems, showcasing their versatility across various domains. Furthermore, we explore applications in image analysis and computer vision, where tensors are effectively utilized for motion and object tracking, 3D modeling, satellite image analysis, and medical imaging. By bridging theoretical advancements with practical applications, this survey aims to guide researchers in leveraging tensor methods to tackle emerging challenges in signal processing.
期刊介绍:
Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing.
Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.