Content Gazette - April 2020 - 10
CALL FOR PAPERS
IEEE Journal of Selected Topics in Signal Processing
Special Issue on Tensor Decomposition for Signal Processing
and Machine Learning
Tensor decomposition, also called tensor factorization, is very useful for representing and
analyzing multidimensional data. Tensor decomposition has been applied in signal processing
(speech, audio, communications, radar, biomedicine), machine learning (clustering, dimensionality
reduction, latent factor models, subspace learning), and beyond. Tensor decomposition helps us to
learn a variety of models, including community models, probabilistic context-free-grammars,
the Gaussian mixture model, and two-layer neural networks.
The multidimensional nature of the signals and even "bigger" data provide a good opportunity to
exploit tensor-based models and tensor network, with the aim of meeting the strong requirements
on flexibility, convergence, and efficiency. Although considerable research has been done on this
subject, there are many challenges still outstanding that need to be explored, like high
computational cost of algorithms, tensor deflation, massive tensor decomposition, etc. 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 in big
data, social network, biomedical and healthcare, advanced data-driven information and
communication technology (ICT) systems and others.
Potential topics include, but are not limited, to the following:
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New tensor decompositions and uniqueness issues of tensor models
Low-rank approximations
Fast and robust tensor decompositions
Novel algorithms for existing tensor decomposition models
Optimization problems related to tensor models
Tensor-based detection and parameter estimation
Tensor decomposition for 5G/B5G wireless communications
Tensor-based data-driven networking
Tensor processing and analysis in social networks
Tensor decomposition for industry internet of things
Spatial temporal data via tensor factorization
Computer vision with tensor method
Biomedical, healthcare, and audio signal processing with tensors
Pattern recognition and neural networks with tensor decomposition
Submission Guidelines:
Prospective authors should follow the instructions on the IEEE JSTSP webpage
https://signalprocessingsociety.org/publications-resources/ieee-journal-selected-topics-signalprocessing and submit their manuscripts at http://mc.manuscriptcentral.com/jstsp-ieee.
Important Dates:
Manuscript submissions due: July 1, 2020
First review due: September 1, 2020
Revised manuscript due: November 1, 2020
Second review due: December 15, 2020
Final manuscript due: January 15, 2021
Guest editors:
Hongyang Chen (Lead GE), Fujitsu Ltd, Japan (hongyang.chen@fujitsu.com)
Sergiy Vorobyov, Aalto University, Finland (svor@ieee.org, sergiy.vorobyov@aalto.fi)
Hing Cheung So, City University of Hong Kong, China (hcso@ee.cityu.edu.hk)
Fauzia Ahmad, Temple University, Philadelphia, USA (fauzia.ahmad@temple.edu)
Fatih Porikli, Australian National University, Australia (fatih.porikli@anu.edu.au)
www.signalprocessingsociety.org
[10]
APRIL 2020
https://www.signalprocessingsociety.org/publications-resources/ieee-journal-selected-topics-signal-processing
https://www.signalprocessingsociety.org/publications-resources/ieee-journal-selected-topics-signal-processing
http://mc.manuscriptcentral.com/jstsp-ieee
http://www.signalprocessingsociety.org
Content Gazette - April 2020
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