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Advanced Data Representation and Learning in Multimedia Data Analysis

Call for Papers

Data representation and representation learning have significantly advanced the big data applications in medicine, social science, economics, engineering, and other disciplines. The research on data representation and learning aims to uncover intrinsic knowledge underlying big data, which can form a basis for further enhancement and analysis. In recent years, this research field has achieved significant advancements and presented various techniques such as subspace learning, manifold learning, sparse coding, multiview learning, feature learning, and deep learning. These achievements have created new opportunities in the areas including multimedia data analysis, large-scale face detection/recognition, intelligent video surveillance, web-scale image/video retrieval and annotation, and human behavior analysis. An in-depth study into data representation and learning can reveal approaches to handle unknown data and help visualize it.

The special issue hunts for original research results on data representation and representation learning in multimedia data analysis. The goals are threefold: (1) building novel data representation and representation learning models for multimedia data analytics, (2) developing efficient algorithms for handling large-scale data representation and representation learning tasks in large-scale multimedia data analysis, and (3) defining new large-scale multimedia data driven applications, which can be cleared up by advance data representation and representation learning techniques.

Potential topics include but are not limited to the following:

  • Multiview learning
  • Manifold learning
  • Subspace learning
  • Deep learning
  • Reinforcement learning
  • Transfer learning
  • Speech and audio analytics
  • Image and video analytics
  • Object and human detection
  • Multimedia retrieval
  • Video surveillance
  • Human behavior analysis

Authors can submit their manuscripts through the Manuscript Tracking System at

Manuscript DueFriday, 25 August 2017
First Round of ReviewsFriday, 17 November 2017
Publication DateFriday, 12 January 2018

Lead Guest Editor

  • Weifeng Liu, China University of Petroleum, Qingdao, China

Guest Editors