【Image and Vision Computing】Special issue on Regularization Techniques for High-
http://www.journals.elsevier.com/image-and-vision-computing/call-for-papers/special-issue-on-regularization-techniques-for-high-dimensioCall for paper of a special issue of Image and Vision ComputingTitle: Regularization Techniques for High-Dimensional Data AnalysisThe explosive growth of high-dimensional visual data in computer vision requires effective techniques to reveal the underlying low-dimensional structure and discover the latent knowledge. Over the past decades, a variety of approaches for visual data modeling and analysis have been proposed, and representative methods include manifold learning, matrix factorization, subspace learning,sparse coding, and deep learning. However, these methods don’t perform well inmany practical applications when visual data contain various corruptions.Moreover, some of them are less theoretically interpretable. Recentdevelopments in regularization techniques have proven their effectiveness inhigh-dimensional visual data analysis, which are also with good interpretabilitiesin statistics. However, numerous problems in regularization techniques arestill unsolved and promising results may be achieved by incorporating theregularization techniques into the classical data modelling methods. Boththeoretical and technical developments are desirable to provide new insightsand tools in modelling the complexity of real world.
Thisspecial issue provides a forum for researchers all over the world to discusstheir works and recent advances in theory, algorithms, and applications forhigh-dimensional visual data analysis. we will invite one survey paper, whichwill undergo peer review. Papers addressing interesting real-world visualcomputing applications are especially encouraged. Topics relevant to thisspecial issue include, but are not limited tol Modellinghigh-dimensional visual data with sparse, low rank, and other regularizedmethodsl Unsupervised,semi-supervised, and supervised regularized methods l Dimensionreduction and subspace clustering with regularized methodsl Deepextensions of the existing regularized methodsl Largescale high-dimensional visual data modelling and analysis methodsl Robustrepresentation learning in high-dimensional datal Multipledata modelling methods integrationl Real-worldapplications based on learning a compact model, e.g., face verification, metriclearning, object classification, scene recognition, multi-media dataabstraction, motion segmentation, etc.l Novelmachine learning approaches for recovering the underlying low-dimensionalstructure from high-dimensional data, e.g., model selection, data completion,intrinsic dimensionality analysis, etc.l Surveypapers regarding the topic of visual data analysis with regularization methods
Tentative Timetable:l Paper submission: Jun. 15, 2016l First notification: July 30, 2016l Revision: Sep. 1,2016l Final decision: Oct. 1, 2016l Publication: Dec.15, 2016
Instructions for submission:l The submission website for this journal is located at: http://ees.elsevier.com/imavis/default.asp l To ensure that all manuscripts are correctly identified for inclusion into the special issue you are editing, it is important that authors select ‘SI:Regularization Techniques’ when they reach the “Article Type” step in the submission process. Please make sure authors are given this instruction when you send out invitation letters and/or instructions to potential authors.
Guest Editors:Jiwen Lu, TsinghuaUniversity, China, E-mail: elujiwen@gmail.comXi Peng, Institute for Infocomm., Research Agency for Science, Technology andResearch (A*STAR) Singapore, Email: pangsaai@gmail.comWeihong Deng, Beijing University of Posts and Telecommunications,China, Email: whdeng@bupt.edu.cnAjmal Mian, School of Computer Science andSoftware Engineering, The University of Western Australia, Australia, Email: ajmal.mian@uwa.edu.au
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