A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction. (April 2020)

• Record Type: Journal Article
• Authors: Gan, Chenquan; Mao, Junwei; Zhang, Zufan; Zhu, Qingyi
• Is Part Of: International journal of distributed sensor networks. Volume 16:Number 4(2020)
• Publication Date: 2020-04
• Subjects: Tensor signal compression -- Tucker decomposition -- sparse representation -- dictionary learning -- denoising ability; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147720916408
• Languages: English
• ISSNs: 1550-1329

Abstract

Tensor compression algorithms play an important role in the processing of multidimensional signals. In previous work, tensor data structures are usually destroyed by vectorization operations, resulting in information loss and new noise. To this end, this article proposes a tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction, which mainly includes three parts: tensor dictionary representation, dictionary preprocessing, and dictionary update. Specifically, the tensor is respectively performed by the sparse representation and Tucker decomposition, from which one can obtain the dictionary, sparse coefficient, and core tensor. Furthermore, the sparse representation can be obtained through the relationship between sparse coefficient and core tensor. In addition, the dimensionality of the input tensor is reduced by using the concentrated dictionary learning. Finally, some experiments show that, compared with other algorithms, the proposed algorithm has obvious advantages in preserving the original data information and denoising ability.