Call For Papers Neurocomputing Special Issue on Dimensionality Reduction for Visual Big Data Website: http://www.journals.elsevier.com/neurocomputing/call-for-papers/dimensionality-reduction-for-visual-big-data Submission Website: http://ees.elsevier.com/neucom/. Please select "SI: Visual Big Data" as their Article Type Submission Deadline: July 1, 2014 Aims and ScopeThe emergence of “big data” has brought about a paradigm shift throughout computer science, such as the fields of computer vision, machine learning and multimedia analysis. Visual big data, which is specifically on visual information such as images and videos, accounts for a large and important part in big data. Lots of theories and algorithms have been developed for visual big data in recent years. Dimensionality reduction techniques, which aim at finding and exploiting low-dimensional structures in high-dimensional data, are playing an increasingly important role in the analytics of visual big data, not only in overcoming the curse of dimensionality, but also in saving the computation and storage burden. Indeed, as the volume of such visual big data increases, scientists are interested in addressing increasingly complex problems - particularly how to account for spatio-temporal data analysis, how to make reduction algorithms efficient and scalable and how to adapt them to new applications. Unfortunately, conventional statistical and computational tools are often severely inadequate for processing and analyzing this kind of large-scale, multi-source and high-dimensional visual big data. Fueled by the availability of abundant contextual and social information, metadata, and geo-tagging, recent years have seen progress in advanced dimensionality reduction methods for visual big data. This special issue targets a mixed audience of researchers from several communities, including machine learning, computer vision, multimedia analysis, data mining, social networks, etc. The marriage between “dimensionality reduction” and “visual big data” will bring huge opportunities as well as challenges to these communities. We believe this special issue will offer a timely collection of novel research results to benefit the researchers and practitioners working in these communities. Topics of Interest This special issue is devoted to the publications of high quality papers on technical developments and practical applications around advanced dimensionality reduction techniques for visual big data. It will serve as a forum for recent advances in the fields of multimedia analysis, computer vision, machine learning, etc. We invite original and high quality submissions addressing all aspects of these fields. Relevant topics include, but are not limited to, the following: Supervised /unsupervised /semi-supervised dimensionality reduction for visual big data Subspace learning for visual big data Manifold learning for visual big data Tensor analysis for visual big data Deep learning for visual big data Non-negative matrix factorization for visual big data Kernel-based dimensionality reduction for visual big data Sparse Representation for visual big data Transfer learning for visual big data Incremental learning for visual big data Efficient learning algorithms for visual big data Binary coding and hashing for visual big data Applications of dimensionality reduction for visual big data.Submission Details Authors should prepare their manuscripts in the Neurocomputing publishing format according to the Guide for Authors available from the online submission page of Neurocomputing at http://ees.elsevier.com/neucom/. Please select "SI: Visual Big Data" as their Article Type during submission. All submitted papers will be peer-reviewed following the Neurocomputing reviewing procedures. Important Dates Paper Submission: Jul. 1, 2014 First Round Notification: Aug. 1, 2014 Revision: Sep. 1, 2014 Final Decision: Oct. 1, 2014 Publication Date: Dec. 1, 2014 Guest Editors Yanwei Pang Professor School of Electronic Information Engineering Tianjin University, Tianjin 300072, China, pyw@tju.edu.cn Ling Shao Associate Professor Department of Electronic & Electrical Engineering University of Sheffield, United Kingdom ling.shao@sheffield.ac.uk