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http://dx.doi.org/10.5392/IJoC.2016.12.3.022

Recovering Incomplete Data using Tucker Model for Tensor with Low-n-rank  

Thieu, Thao Nguyen (Department of Electronics and Computer Engineering, Chonnam National University)
Yang, Hyung-Jeong (Department of Electronics and Computer Engineering, Chonnam National University)
Vu, Tien Duong (Department of Electronics and Computer Engineering, Chonnam National University)
Kim, Sun-Hee (Department of Brain and Cognitive Engineering, Korea University)
Publication Information
International Journal of Contents / v.12, no.3, 2016 , pp. 22-28 More about this Journal
Abstract
Tensor with missing or incomplete values is a ubiquitous problem in various fields such as biomedical signal processing, image processing, and social network analysis. In this paper, we considered how to reconstruct a dataset with missing values by using tensor form which is called tensor completion process. We applied Tucker factorization to solve tensor completion which was built base on optimization problem. We formulated the optimization objective function using components of Tucker model after decomposing. The weighted least square matric contained only known values of the tensor with low rank in its modes. A first order optimization method, namely Nonlinear Conjugated Gradient, was applied to solve the optimization problem. We demonstrated the effectiveness of the proposed method in EEG signals with about 70% missing entries compared to other algorithms. The relative error was proposed to compare the difference between original tensor and the process output.
Keywords
Tucker Decomposition; Missing Value and Tensor Completion;
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