Call for Papers: Special Issue on Multi-view Learning


With advances in information acquisition technologies, multi-view data are increasing dramatically in a variety of forms, such as text and sound, text and images, text in different languages, etc. The different views contain complementary and consensus information. To make full use of this information, many multi-view learning paradigms have appeared, such as multi-view classification, multi-view semi-supervised learning, multi-view clustering, multi-view representation learning, and a variety of deep learning techniques.

Although multi-view learning has gained great success in the past two decades, there are still many open problems that require further investigation. For example, most existing multi-view learning methods cannot deal with missing values directly. Multi-view clustering lacks stability due to initialization. The big data era calls for more scalable and efficient multi-view approaches. Deep learning technology may bring some new strategies to integrate multiple views. All these call for novel multi-view learning theory and algorithms.

Multi-view learning has been successfully applied to subfields in many applications like computer vision, natural language processing, social network, health, biology, economics, marketing, finance. Extending its application to more subfields needs more novel advanced multi-view learning theory and algorithms, especially deep ones.  

Scope and Topics of Interest

We welcome novel, innovative, state-of-the-art, original, creative, applicable, and cutting-edge contributions on the following topics, but not limited to:

  • Multi-view feature representation learning
  • Large scale multi-view learning algorithm design
  • Multi-view learning approaches to deal with missing values and noisy samples
  • Novel theory for multi-view learning
  • Multi-view learning for graphs and relational data 
  • New strategies to integrate multiple views
  • Views weighting or views selection
  • Visualization of multi-view data
  • Multi-view learning and its application in medical informatics, computer vision, natural language processing, etc. 
  • Relationship to transfer learning, multi-task learning, ensemble learning
  • New High quality multi-view data sets
  • Multi-view learning for pandemic and covid-19 data modeling

All accepted manuscripts are expected to solve or partially solve one of the open problems in multi-view learning or inspire new ideas to help advance multi-view learning further. Each of the submitted papers will be reassigned at least three reviewers nominated by the committee of the proposed editors of this special issue.

Important Dates

Submission deadline: July 10, 2021

First notification: August 30, 2021

Second notification: October 30, 2021

Final decision: November 30, 2021

For questions and further information, please contact Guoqing Chao at

Managing Guest Editor:

Dr. Guoqing Chao, Associate Professor, Harbin Institute of Technology, China (, Google Scholar

Guest Editors:

Dr. Xingquan Zhu, Professor, Florida Atlantic University, USA (, Google Scholar

Dr. Weiping Ding, Professor, Nantong University, China (, Google Scholar

Dr. Jinbo Bi, Professor, University of Connecticut, USA (, Google Scholar

Dr. Shiliang Sun, Professor, East China Normal University, China (, Google Scholar


Guoqing Chao, Associate Professor, received the Ph.D. degree in Computer Application, East China Normal University (ECNU), Shanghai, China. From 2015 to 2017, he was a Postdoc Fellow at University of Connecticut (UCONN), Storrs, USA. From 2017 to 2018, he was a Postdoc Fellow at Northwestern University (NU), Chicago, USA. From 2018 to 2020, he was a research Scientist at Singapore Management University (SMU), Singapore. Currently, he is an associate professor at Harbin Institute of Technology, Weihai, China. His main research interest includes machine learning, data mining, medical informatics, bioinformatics and service computing. He has published more than 10 papers as first author in journals including TNNLS, Information Fusion, Information Sciences and conferences including IJCAI, BIBM, ICONIP, etc.

He had organized workshops in BIBM 2016, BIBM 2017. He currently serves as the Program Chair, or Program Committee Member of several international conferences in the area of artificial intelligence, data mining, machine learning, medical informatics, such as AAAI 2017-2021, ICONIP 2016-2021. He is the active reviewers for journals like TKDE, TNNLS, T-CYB, TFS, TKDD, Information Fusion, Information Sciences, Pattern Recognition, Knowledge based Systems, Neurocomputing and so on.

Affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Weihai, 264209, China

Author Name: Guoqing Chao


Xingquan Zhu

is a Full Professor in the Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University. His research interests mainly include artificial intelligence, machine learning, and bioinformatics. Since 2000, he has published over 260 refereed journal and conference papers in these fields, and these publications have received over 15,000 citations.

Dr. Zhu is an Associate Editor of the ACM Transactions on Knowledge Discovery from Data (2017-date) and an Associate Editor of the IEEE Transactions on Knowledge and Data Engineering (2008-2012, 2014-date). He is currently serving on the Editor Board of International Journal of Social Network Analysis and Mining SNAM (2010-date), Journal of Big Data (2013-date), and Network Modeling Analysis in Health Informatics and Bioinformatics Journal (2014-date). He is a conference co-chair for IEEE BigData-2021 and served as a conference co-chair for IEEE ICMLA-2012. He has served (or is serving) as Program Committee Co-Chairs and Program Committee Vice Chair (Senior PC) for many international conferences including ACM SIGKDD, AAAI, IEEE ICDM, etc.

During his career, Dr. Zhu has received multiple awards, including three Best Paper Award (IRI-2018, PAKDD-2013, ICTAI-2005), and three Best Student Paper Award (ICDM-2020, ICKG-2020, ICPR-2012), Outstanding Engineering Achievement Merit Award (Engineers’ Council), Senior Faculty Research Award (FAU College of Engineering and Computer Science), and several Service Awards.

Affiliation: Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA

Author Name: Xingquan Zhu


Weiping Ding (M’16-SM’19) received the Ph.D. degree in Computation Application, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China, in 2013. He was a Visiting Scholar at University of Lethbridge(UL), Alberta, Canada, in 2011. From 2014 to 2015, He is a Postdoctoral Researcher at the Brain Research Center, National Chiao Tung University (NCTU), Hsinchu, Taiwan. In 2016, He was a Visiting Scholar at National University of Singapore (NUS), Singapore. From 2017 to 2018, he was a Visiting Professor at University of Technology Sydney (UTS), Ultimo, NSW, Australia. Now, Dr. Ding is the Chair of IEEE CIS Task Force on Granular Data Mining for Big Data. He is a member of Senior IEEE, IEEE-CIS, ACM, CCAI and Senior CCF. He is a member of Technical Committee on Soft Computing of IEEE SMCS, on Granular Computing of IEEE SMCS, and on Data Mining and Big Data Analytics of IEEE CIS. He is currently a Full Professor with the School of Information Science and Technology, Nantong University, Nantong, China. His main research directions involve data mining, granular computing, evolutionary computing, machine learning and big data analytics. He has published more than 80 research peer-reviewed journal and conference papers in this field, including IEEE T-FS, T-NNLS, T-CYB, T-SMCS, T-BME, T-II, T-ETCI and T-ITS, etc, and he has held 15 approved invention patents. His four co-authored papers have been selected as ESI Highly Cited Papers. Dr. Ding was an Excellent-Young Teacher (Qing Lan Project) of Jiangsu Province in 2014, a High-Level Talent (Six Talent Peak) of Jiangsu Province in 2016, and a Middle-aged and Young Academic Leaders (Qing Lan Project) of Jiangsu Province in 2019. He was awarded the Best Paper of ICDMA’15. Dr. Ding was a recipient of the Medical Science and Technology Award (Second Prize) of Jiangsu Province, China, in 2017, and the Education Teaching and Research Achievement Award (Third Prize) of Jiangsu Province, China, in 2018. He was the Outstanding Associate Editor of 2018 for IEEE Access Journal. Dr. Ding was awarded two Chinese Government Scholarships for Overseas Studies in 2011 and 2016.

Dr. Ding is vigorously involved in editorial activities. He currently serves on the Editorial Advisory Board of Knowledge-Based Systems (Elsevier) and Editorial Board of Information Fusion (Elsevier), Applied Soft Computing (Elsevier), and Neurocomputing (Elsevier). He serves as an Associate Editor of IEEE Transactions on Fuzzy Systems, Information Sciences (Elsevier), Swarm and Evolutionary Computation (Elsevier), IEEE Access and Journal of Intelligent & Fuzzy Systems, and Co-Editor-in-Chief of Journal of Artificial Intelligence and System. He is the Leading Guest Editor of Special Issues in several prestigious journals, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, Information Fusion, Information Sciences, and so on. He has delivered more than 20 keynote speeches at international conferences and has served as the Program Chair, Workshop Chair, or Program Committee Member of several international conferences and symposiums in the area of data mining, fuzzy decision-making, and knowledge engineering, such as IEEE SMC2018, ICDM 2019, IEEE SSCI 2019, IEEE CEC 2019, IJCNN 2019, IEEE BigData 2020, FUZZ-IEEE 2020, IJCNN 2021, CEC2021, IJCAI 2021, and so on.

Affiliation:  School of Information Science and Technology, Nantong University, Nantong 226019, China

Author Name: Weiping Ding


Jinbo Bi, Frederick H Leonhardt Professor of Computer Science at the University of Connecticut (UConn), received a Ph.D. degree in Mathematics from Rensselaer Polytechnic Institute, USA, and a M.S. degree in Automatic Control from Beijing Institute of Technology, China. She is currently also an associate head of the Department of Computer Science and Engineering at the UConn. She has had 18 years of experience in developing machine learning approaches to meet life science challenges. First in industry (2003-2010), she conducted cutting-edge research for cancer detection from imaging modalities at Siemens and clinical decision support for trauma patient care at Massachusetts General Hospital at Partners Healthcare. Then in an academic setting at the UConn since 2010, she has been designing innovative and multidisciplinary approaches for computational genomics and refined classification of complex disorders, particularly psychiatric disorders with advanced machine learning and computer vision techniques.

She has authored over 100 peer-reviewed articles in leading journals and conferences in the respective fields, including prestigious machine learning journals such as the Journal of Machine Learning Research and IEEE Transactions on Pattern Analysis and Machine Intelligence, and medical journals such as BMC Genomics, BMC Medical Genomics, and the American Journal of Medical Genetics. Her work in bioinformatics and machine learning has been internationally recognized as innovative and methodologically rigorous, as evidenced by best paper awards (e.g., IEEE BIBM2014) and invited presentations in international forums. Her research findings have been presented in many first-tier international conferences such as International Conference on Machine Learning (ICML), ACM International Conference on Knowledge Discovery and Data mining (KDD), Neural Information Processing Systems (NIPS), and IEEE International Conference on Bioinformatics and Biomedicine (BIBM). She has, annually since 2008, served on program committees for ICML, KDD and NIPS, and recently served as area chair or senior PC for AAAI and IJCAI. She was also an editor for Computers in Biology and Medicine, the International Journal of Bioinformatics Research, the British Journal of Health Informatics and the Journal of Machine Learning Research. She organized the workshops on multi-view subtyping methods to categorize complex disease in IEEE BIBM 2014, 2015, and 2016.

She is Principal Investigator of two large multi-site NIH R01 projects and also a recipient of NIH’s K02 mid-career investigator award. She has had continuous research funding from NSF to develop theory and foundations for advanced machine learning analysis, especially multi-modal/multi-view unsupervised and supervised learning models, longitudinal data analytics, parallel/distributed and federated learning algorithms to enable the concurrent analysis of massive genomic data, behavioral, physiological features, and diagnostic images to predict an individual’s risk for a disorder. Overall, her research interests include machine learning, data mining, bioinformatics and biomedical informatics, computer vision, optimization, and drug discovery. Her google citation is 5117, H-index is 33 and H10-index is 75.

Affiliation: Department of Computer Science & Engineering, School of Engineering
University of Connecticut, Storrs, 371 Fairfield Way, Unit 4155, Storrs, CT 06269-4155

Author Name: Jinbo Bi


Shiliang Sun, received the B. E. degree from Beijing University of Aeronautics and Astronautics (BUAA), and the M. E. and Ph.D. degrees in Pattern Recognition and Intelligent Systems from Tsinghua University. In 2004, he was awarded Microsoft Fellowship. In 2007, he joined the Department of Computer Science and Technology, East China Normal University (ECNU), and founded the Pattern Recognition and Machine Learning (PRML) Research Group. From 2009 to 2010, he was a visiting researcher at the Centre for Computational Statistics and Machine Learning (CSML) and the Department of Computer Science, University College London (UCL). From March to April 2012, he was a visiting researcher at the Department of Statistics and Biostatistics, Rutgers University. In July 2014, he was a visiting researcher at the Department of Electrical Engineering, Columbia University. His research interest include Probabilistic Models and Inference Techniques, Bayesian Nonparametric Models, Optimization Methods, Large-Scale Machine Learning, Statistical Learning Theory and Kernel Methods, Multiview Data Analysis, Sequential and Structural Data Modeling. He has published more than 100 research peer-reviewed journal and conference papers in this field, including IEEE T-PAMI, JMLR, T-NNLS, T-CYB, Information Fusion, PR and T-ITS, etc, and His two co-authored papers have been selected as ESI Highly Cited Papers. His google citation is 6264, h-index is 40 and h10-index is 117.

Dr. Sun is vigorously involved in editorial activities. He was a program co-chair for ICONIP 2017 and is a member of the PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) network of excellence. He is also an associate editor in journals IEEE Transactions on Neural Network and Learning Systems (TNNLS), Information Fusion, Pattern Recognition (PR).

Affiliation: Dept. of Computer Science and Technology, East China Normal University

3663 North Zhongshan Road, Shanghai 200062, P. R. China

Author Name: Shiliang Sun

Email: (preferred),