Deep Multi-source Data Analysis (DMDA)

in Special Issue   Posted on June 21, 2019 

Information for the Special Issue

Submission Deadline: Fri 31 Jan 2020
Journal Impact Factor : 3.255
Journal Name : Pattern Recognition Letters
Journal Publisher:
Website for the Special Issue:
Journal & Submission Website:

Special Issue Call for Papers:

Summary and Scope:

Internet makes data acquisition easy and cheap, leading to multi-source data pervasive in the real life. Multi-source data provides enough information that often makes the models can be learned effectively. However, multi-source data is also complex, heterogeneous, and very large in size where inappropriate handling of it will produce ineffective learning models. This inevitably causes the multi-source data analysis a challenging task in many applications. The conversional shallow analysis techniques have been shown to be difficult in dealing with big multi-source data due to their massive volume and multi-source structure, while the most popular deep analysis techniques encounter a lot of limitations (e.g., huge computation power and huge numbers of tuning parameters) in order to make it proficient in the specific domains. Therefore, the study of Deep Multi-source Data Analysis (DMDA) (including novel shallow learning techniques, advanced deep learning techniques, and especially their hybrid) has been a very popular topic in the domain of machine learning and computer vision.

In this special issue, we invite papers to address many challenges of big multi-source data analysis. Specifically, to provide readers of this special issue with state-of-the-art background on the topic, we will invite one survey paper, which will undergo the peer review process. The list of possible topics include, but not limited to:

  • Multi-source transaction data analysis
  • Data preprocess of multi-source databases (missing value imputation and feature selection, clustering, and synthesizing/fusion) via shallow learning techniques and deep learning techniques
  • Distributed/paralleled techniques and sampling techniques for big multi-source databases mining
  • Transfer learning among multi-source database
  • Multi-source multimedia data analysis
  • Representation learning (e.g., deep learning methods, local feature extraction methods, and global feature extraction methods)
  • Multi-source data analysis tools and applications (e.g. search, storing, ranking, hashing, and retrieval)
  • Structured/semi-structured multi-source data analysis (e.g., zero-shot learning, one-shot learning, supervised learning, unsupervised learning and semi-supervised learning)
  • Cross-model data analysis (e.g. search and retrieval) via transfer learning and deep learning
  • Multi-task data analysis
  • Similarities/dissimilarities learning from multiple tasks
  • Regularization strategies in multi-task learning or domain adaptation and transfer learning
  • Multi-task learning or domain adaptation or transfer learning for big computer vision and multimedia analysis
  • Large tasks (modals), small sample size learning for multi-task learning, domain adaptation, and transfer learning

Submission Guideline

Authors should prepare their manuscript according to the Guide for Authors available from the online submission page of Pattern Recognition Letters at Specifically, authors should find the acronym of the special issue visible to be selected as article type “DMDA”. The maximal length of the submissions should be at most 7 pages in the template of Pattern Recognition Letters which can be found in Moreover, all the submissions should be original and technically sound.

All papers will be peer-reviewed by at least two independent reviewers. Requests for additional information should be addressed to the guest editors.

Important Dates:

  • Paper submission period: January 1-31, 2020
  • First notification: May 1-15, 2020
  • Second notification: August 1-15, 2020
  • Final decision: November 1-15, 2020

Guest Editors:

  • Dr. Shichao Zhang, Central South University, China ( (Leading Guest Editor)
  • Dr. Qing Xie, School of Computer Science and Technology, Wuhan University of Technology, China (
  • Dr. Yanrong Guo, Hefei University of Technology, China (

Other Special Issues on this journal

Closed Special Issues