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Tensor Decomposition for Signal Processing and Machine Learning
2017
IEEE Transactions on Signal Processing
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processing, statistics, data mining and machine learning. ...
This overview article aims to provide a good starting point for researchers and practitioners interested in learning about and working with tensors. ...
algorithms, and the basic ways in which tensor decompositions are used in signal processing and machine learning -and they are quite different. ...
doi:10.1109/tsp.2017.2690524
fatcat:lw54gmheezaehazy7kvn6qmmwa
IEEE Journal of Selected Topics in Signal Processing Special Issue on Tensor Decomposition for Signal Processing and Machine Learning
2020
IEEE Signal Processing Magazine
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The goal of this special issue is to attract high quality papers containing original research on tensor methods, tensor decompositions for signal processing and machine learning, and their applications ...
Tensor decomposition has been applied in signal processing (speech, audio, communications, radar, biomedicine), machine learning (clustering, dimensionality reduction, latent factor models, subspace learning ...
Submission Guidelines: Prospective authors should follow the instructions on the IEEE JSTSP webpage https://signalprocessingsociety.org/publications-resources/ieee-journal-selected-topics-signalprocessing and ...
doi:10.1109/msp.2020.2999174
fatcat:cqclqtmu35bxpjfvpptnab6qt4
Introduction to the Special Issue on Tensor Decomposition for Signal Processing and Machine Learning
2021
IEEE Journal on Selected Topics in Signal Processing
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Introduction to the Special Issue on Tensor Decomposition for Signal Processing and Machine Learning T ENSOR decomposition, also called tensor factorization, is useful for representing and analyzing multi-dimensional ...
This special issue (SI) covers a broad array of research on tensor methods, tensor decompositions for signal processing and machine learning, and their applications in wireless communications, radar, biomedicine ...
doi:10.1109/jstsp.2021.3065184
fatcat:qbvihejwkfaa5hoztety77pnwi
YangX_TensorMethod.pdf
2017
Figshare
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Recently, tensor methods have been applied in signal/image processing and machine learning [3] . Dictionary learning is a relatively recent method in data analysis related to frame theory [4] . ...
Recently, Google released a new processing unit -Tensor Processing Unit (TPU) -designed specifically for machine learning based on tensors. ...
doi:10.6084/m9.figshare.5318134.v1
fatcat:zn6mybypvjhnpjnjis555qnghe
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
2016
Foundations and Trends® in Machine Learning
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It is therefore timely and valuable for the multidisciplinary research community to review tensor decompositions and tensor networks as emerging tools for large-scale data analysis and data mining. ...
Machine learning and data mining algorithms are becoming increasingly important in analyzing large volume, multi-relational and multi--modal datasets, which are often conveniently represented as multiway ...
Papalexakis,
and C. Faloutsos. Tensor decomposition for signal processing and machine
learning. arXiv e-prints arXiv:1607.01668, 2016.
A. Smilde, R. Bro, and P. Geladi. ...
doi:10.1561/2200000059
fatcat:ememscddezeovamsoqrcpp33z4
Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
2020
Applied Sciences
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We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets. ...
In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial ...
Acknowledgments: We are grateful to the anonymous reviewers for their valuable comments, which helped us to improve the first version of this manuscript. ...
doi:10.3390/app10238481
fatcat:2gqm3tos4vdorptqayewqu2mum
Table of Contents
2021
IEEE Journal on Selected Topics in Signal Processing
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Introduction to the Special Issue on Tensor Decomposition for Signal Processing and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Giampouras 464 Tensor Decomposition Learning for Compression of Multidimensional Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/jstsp.2021.3061240
fatcat:j3uioq5xh5hdxg2uitjqeszh3m
Tensor Decompositions in Deep Learning
[article]
2020
arXiv
pre-print
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The paper surveys the topic of tensor decompositions in modern machine learning applications. It focuses on three active research topics of significant relevance for the community. ...
After a brief review of consolidated works on multi-way data analysis, we consider the use of tensor decompositions in compressing the parameter space of deep learning models. ...
Section 3 reviews classical applications of tensor decompositions to multi-way signal processing and data analysis. ...
arXiv:2002.11835v1
fatcat:izu4qtizqbghhnaxlhisi2tnce
Spectral Learning on Matrices and Tensors
2019
Foundations and Trends® in Machine Learning
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Spectral methods have been the mainstay in several domains such as machine learning and scientific computing. ...
This data pre-processing step is often effective in separating signal from noise. PCA and other spectral techniques applied to matrices have several limitations. ...
More recently, Sidiropoulos et al. (2017) provide an overview of different types of tensor decompositions and some of their applications in signal processing and machine learning. ...
doi:10.1561/2200000057
fatcat:ps6zp5nbcfdpzdwe57fpc4xkta
Introduction to the Issue on Robust Subspace Learning and Tracking: Theory, Algorithms, and Applications
2018
IEEE Journal on Selected Topics in Signal Processing
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Many problems in signal processing and machine learning can be posed as the problem of learning lower dimensional representation of the data. ...
Her research interests lie at the intersection of signal and information processing, machine learning for high dimensional problems, and computer vision.Paul Rodriguez is currently a Full Professor with ...
doi:10.1109/jstsp.2018.2879245
fatcat:z3ohqdl37nat3pjo65fzsf2ady
Noninvasive BCIs: Multiway Signal-Processing Array Decompositions
2008
Computer
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and on signal-processing, machine-learning, and data-mining tools to analyze the data and extract useful information. ...
Next, we need to optimize the measurement procedure and develop real-time signal-processing algorithms that decode and interpret the resulting brain signals. ...
To tackle the complex challenge of electrophysiological signal analysis and discrimination, we need to employ advanced signal-processing, multidimensional data mining, and machine-learning tools and their ...
doi:10.1109/mc.2008.431
fatcat:auvhydbdcnagdehqhjbmxxgnnm
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection
[article]
2021
arXiv
pre-print
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In this work, we propose a powerful data-driven and quasi non-parametric denoising strategy for spectral data based on a tensor decomposition: the Non-negative Canonical Polyadic (CP) decomposition. ...
Acoustic monitoring for machine fault detection is a recent and expanding research path that has already provided promising results for industries. ...
Deep learning based methods have recently shown remarkable performances for unsupervised detection problem (and more generally for audio signal processing (Purwins et al., 2019) ). ...
arXiv:2107.09519v1
fatcat:i65sea3hnjc57moy4neisqnttm
Fault Diagnosis of Air Compressor in Nuclear Power Plant Based on Vibration Observation Window
2020
IEEE Access
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ACKNOWLEDGMENT We thank the great help from Southeast University and Beckhoff Automation in this research. ...
For a classification process based on classical machine learning method, the first step is feature extraction. ...
and classical machine learning. ...
doi:10.1109/access.2020.3043398
fatcat:fnorkx3xhvhvfcowfa72ev67iq
Tensor Computing for Internet of Things (Dagstuhl Perspectives Workshop 16152)
2018
Dagstuhl Manifestos
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The multidisciplinary discourse among academics, industrial researchers and practitioners in the IoT/CPS domain and in the field of machine learning and tensor methods, exposed open issues that need to ...
The tools and frameworks used to engineer such systems will ensure production-ready machine learning code which utilizes tensor-based, hence better interpretable, models and runs on distributed, decentralized ...
specifically for machine learning. ...
doi:10.4230/dagman.7.1.52
dblp:journals/dagstuhl-manifestos/AcarAMRT18
fatcat:5ngo2fzsefbh7e5a4odx4ttgne
mpnum: A matrix product representation library for Python
2017
Journal of Open Source Software
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Tensors -or high-dimensional arrays -are ubiquitous in science and provide the foundation for numerous numerical algorithms in scientific computing, machine learning, signal processing, and other fields ...
This has led to the development of sparse and low-rank tensor decompositions (Kolda and Bader 2009). ...
QUCHIP, and the US Army Research Office Grant No. ...
doi:10.21105/joss.00465
fatcat:7wusapi7gjdpjnuzigkwszvi6a
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