Factorized Tensor Dictionary Learning for Visual Tensor Data Completion

This paper aims at developing a dictionary-learning-based method for completing the visual tensor data with missing elements. Traditional dictionary learning approaches suffer from very high computational costs when processing high-dimensional tensor data. Some existing approaches for acceleration i...

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Bibliographic Details
Published in:IEEE transactions on multimedia Vol. 23; pp. 1225 - 1238
Main Authors: Xu, Ruotao, Xu, Yong, Quan, Yuhui
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Analytical models
Constraints
Convolution
Convolutional codes
convolutional sparse coding
Dictionaries
Encoding
Factorization
factorized dictionary learning
Learning
Machine learning
Orthogonality
Tensile stress
tensor completion
Tensor dictionary learning
Tensors
Visualization
ISSN:1520-9210, 1941-0077
Online Access:Get full text
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Summary:This paper aims at developing a dictionary-learning-based method for completing the visual tensor data with missing elements. Traditional dictionary learning approaches suffer from very high computational costs when processing high-dimensional tensor data. Some existing approaches for acceleration impose orthogonality constraints or rank-one decompositions on dictionary atoms; however, the expressibility of the resulting dictionary is rather limited. To address such issues, we propose a convolutional analysis model for tensor dictionary learning, where the update of sparse coefficients during dictionary learning is simple and fast. Furthermore, we propose an orthogonality-constrained convolutional factorization scheme for dictionary construction, in which each tensor dictionary atom is factorized by the convolution of two atoms selected from two orthogonal factor dictionaries respectively. This factorization scheme enables us to efficiently learn an expressive dictionary with over-completeness and non-rank-one atoms. Based on our convolutional analysis model and factorization scheme, an effective yet efficient dictionary learning method is proposed for visual tensor completion. Extensive experiments show that, our method not only outperforms existing dictionary-based approaches with relatively-low time cost, but also outperforms recent low-rank approaches.
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ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2020.2994512