Machine learning techniques have played a central role in pattern recognition, and a variety of machine learning methods have been developed for various pattern recognition applications over the past decade. Among these learning methods, distance metric learning has achieved many state-of-the-arts in many pattern recognition applications, which aims to learn an appropriate distance function given some constrains between samples. To better discover the geometric property of high-dimensional feature spaces and exploit the complementary information of different feature spaces, manifold learning and multi-view learning strategies have also been integrated into distance metric learning to further improve the performance of various distance metric learning methods. While these methods are helpful to learn the similarity of data such as images, videos, texts, radars, and voices, how to develop task-specific distance metric learning algorithms for different pattern recognition tasks still remains unsolved, especially for big data which are captured in the wild. Moreover, how to develop transferable and nonlinear distance metric learning methods for large-scale pattern recognition systems still requires many efforts.
This special issue serves as a forum for researchers all over the world to discuss their works and recent advances in distance metric learning for pattern recognition. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world pattern recognition applications are especially encouraged. Topics of interest include, but are not limited to:
Semi-supervised distance metric learning for pattern recognition
Multi-view distance metric learning for pattern recognition
Structural distance metric learning for pattern recognition
Domain transfer distance metric learning for pattern recognition
Deep distance metric learning for pattern recognition
Large-scale distance metric learning for pattern recognition
Hashing-based distance metric learning for pattern recognition
Comparative study of distance metric learning for pattern recognition
Paper submission: Sep. 15, 2016
First notification: Dec. 15, 2016
Revision: Jan. 30, 2017
Final decision: Jun. 15, 2017
Publication: Sep, 2017
The submission website for this journal is located at: http://ees.elsevier.com/pr/default.asp.
To ensure that all manuscripts are correctly identified for inclusion into the special issue, it is important that authors select “SI:Distance Metric Learning” when they reach the “Article Type Name” step in the submission process.
Jiwen Lu, Department of Automation, Tsinghua University, China, E-mail: email@example.com
Ruiping Wang, Institute of Computing Technology, Chinese Academy of Sciences, China, Email: firstname.lastname@example.org
Ajmal Mian, School of Computer Science and Software Engineering, The University of Western Australia, Australia, Email: email@example.com
Ajay Kumar, Department of Computing, The Hong Kong Polytechnic University, Hong Kong, Email: firstname.lastname@example.org
Sudeep Sarkar, Department of Computer Science and Engineering, The University of South Florida, USA, Email: email@example.com