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

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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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