Year:    2020

CAM-Net Digest, Vol. 17 (2020), Iss. 24 : p. 8

Abstract

Tensors, also called hypermatrices, are natural extensions of vectors and matrices. In the literature, tensor has been shown to be a powerful tool to represent multi-dimensional data, which can maximally preserve the inherent structure of the data. Therefore, the multidimensional and large scale nature of the tensor data provides a good opportunity to exploit tensor optimization models, with the aim of exploring the inherent structure of tensor data to improve the accuracy and efficiency of big data analysis. In the past decades, data-driven tensor optimization theory and algorithms have been widely used in the areas of machine learning, computer vision, image and video processing, face recognition, data analysis and recovery, and so on.

Although many papers have been contributed to tensor computation in recent years, it is challeng- ing and necessary to develop structure-exploiting tensor optimization models, theory and algorithms for higher-order tensor related problems. The goal of this special issue is to attract original research papers on the theory, algorithms, and applications of tensor optimization. These applications include machine learning, data recovery, data anomaly detection, computer vision, signal processing, image and video processing, face recognition and other data-driven applications.

Topics of the special issue include:
• Tensor completion and recovery with optimization methods
• Data anomaly detection with tensor optimization
• Tensor optimization methods for image processing and signal processing
• Tensor optimization methods for machine learning
• First-order, BCD and convex optimization methods for tensor data problems 
• Sparse optimization methods for tensor data problems
• Tensor norms and ranks as well as their applications in data analysis

Guest Editors:
• Chen Ling, Hangzhou Dianzi University, China. (macling@hdu.edu.cn)
• Yannan Chen, South China Normal University, China. (ynchen@scnu.edu.cn) 
• Hongjin He, Hangzhou Dianzi University, China. (hehjmath@hdu.edu.cn)
• Ziyan Luo, Beijing Jiaotong University, China. (zyluo@bjtu.edu.cn)

Submission Guidelines:
Authors should follow the Instructions for Authors of the Pacific Journal of Optimization from http://yokohamapublishers.jp/pjo.html and submit their manuscripts to one of the Guest Editors' emails. 

Notice that the Deadline of Manuscript submission is March 31, 2021.