Beyond Low Rank: A Data-Adaptive Tensor Completion Method
Zhang, Lei; Wei, Wei; Shi, Qinfeng; Shen, Chunhua; Hengel, Anton van den; Zhang, Yanning;
Subject: Computer Science - Computer Vision and Pattern Recognition
Low rank tensor representation underpins much of recent progress in tensor completion. In real applications, however, this approach is confronted with two challenging problems, namely (1) tensor rank determination; (2) handling real tensor data which only approximately ... View more
 E. Papalexakis, K. Pelechrinis, and C. Faloutsos, “Spotting misbehaviors in location-based social networks using tensors,” in Proceedings of the 23rd International Conference on World Wide Web. ACM, 2014, pp. 551-552.
 L. Yao, Q. Z. Sheng, Y. Qin, X. Wang, A. Shemshadi, and Q. He, “Context-aware point-of-interest recommendation using tensor factorization with social regularization,” in Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2015, pp. 1007-1010.
 X. Guo and Y. Ma, “Generalized tensor total variation minimization for visual data recovery?” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. IEEE, 2015, pp. 3603-3611.
 J. Liu, P. Musialski, P. Wonka, and J. Ye, “Tensor completion for estimating missing values in visual data,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 1, pp. 208-220, 2013.
 Y.-L. Chen, C.-T. Hsu, and H.-Y. M. Liao, “Simultaneous tensor decomposition and completion using factor priors,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 3, pp. 577-591, 2014.
 Q. Zhao, L. Zhang, and A. Cichocki, “Bayesian cp factorization of incomplete tensors with automatic rank determination,” PAMI, vol. 37, no. 9, pp. 1751-1763, 2015.
 B. W. Bader, T. G. Kolda et al., “Matlab tensor toolbox version 2.6,” Available online, February 2015. [Online]. Available: http://www.sandia.gov/ tgkolda/TensorToolbox/
 Q. Gu, H. Gui, and J. Han, “Robust tensor decomposition with gross corruption,” in Proc. Advances in Neural Inf. Process. Syst., 2014, pp. 1422-1430.
 Z. Zhang, G. Ely, S. Aeron, N. Hao, and M. Kilmer, “Novel methods for multilinear data completion and de-noising based on tensor-svd,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn., 2014, pp. 3842-3849.
 Q. Zhao, D. Meng, X. Kong, Q. Xie, W. Cao, Y. Wang, and Z. Xu, “A novel sparsity measure for tensor recovery,” in Proc. IEEE Int. Conf. Comp. Vis., 2015, pp. 271-279.