Results 41 to 50 of about 1,665,186 (233)

Orthogonal decomposition of tensor trains [PDF]

Linear and Multilinear Algebra, 2021
In this paper we study the problem of decomposing a given tensor into a tensor train such that the tensors at the vertices are orthogonally decomposable. When the tensor train has length two, and the orthogonally decomposable tensors at the two vertices are symmetric, we recover the decomposition by considering random linear combinations of slices ...
Halaseh, Karim, Muller, Tommi, Robeva, Elina   +2 more
openaire   +2 more sources

Tensor Completion Using Kronecker Rank-1 Tensor Train With Application to Visual Data Inpainting

IEEE Access, 2018
The problem of data reconstruction with partly sampled elements under a tensor structure, which is referred to as tensor completion, is addressed in this paper.
Weize Sun, Yuan Chen, Hing Cheung So
doaj   +1 more source

Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics

IEEE Transactions on Industrial Informatics, 2021
With the increasing use of induction thermography (IT) for nondestructive testing in the mechanical and rail industry, it becomes necessary for the Manufacturers to rapidly and accurately monitor the health of specimens.
Junaid Ahmed, B. Gao, W. L. Woo
semanticscholar   +1 more source

Generalized Canonical Polyadic Tensor Decomposition [PDF]

SIAM Review, 2020
Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error.
Hong, David, Kolda, Tamara G., Duersch, Jed A.   +2 more
openaire   +2 more sources

Knowledge Graph Reasoning Based on Tensor Decomposition and MHRP-Learning

Advances in Multimedia, 2021
In the process of learning and reasoning knowledge graph, the existing tensor decomposition technology only considers the direct relationship between entities in knowledge graph. However, it ignores the characteristics of the graph structure of knowledge
Tangsen Huang, Xiaowu Li, Sheping Zhai, Juanli Wei   +3 more
doaj   +1 more source

ANLPT: Self-Adaptive and Non-Local Patch-Tensor Model for Infrared Small Target Detection

Remote Sensing, 2023
Infrared small target detection is widely used for early warning, aircraft monitoring, ship monitoring, and so on, which requires the small target and its background to be represented and modeled effectively to achieve their complete separation. Low-rank
Zhao Zhang, Cheng Ding, Zhisheng Gao, Chunzhi Xie   +3 more
doaj   +1 more source

Dictionary-based Tensor Canonical Polyadic Decomposition

, 2017
To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary.
Cohen, Jérémy E., Gillis, Nicolas
core   +1 more source

Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme

Applied Sciences
Employing low tensor rank decomposition in image inpainting has attracted increasing attention. This study exploited novel tensor arrangement schemes to transform an image (a low-order tensor) to a higher-order tensor without changing the total number of
Shuli Ma, Youchen Fan, Shengliang Fang, Weichao Yang, Li Li   +4 more
doaj   +1 more source

A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction

International Journal of Distributed Sensor Networks, 2020
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
Chenquan Gan, Junwei Mao, Zufan Zhang, Qingyi Zhu   +3 more
doaj   +1 more source

Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion

, 2018
In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity.
Cao, Jianting, Li, Chao, Mandic, Danilo, Yuan, Longhao, Zhao, Qibin   +4 more
core   +1 more source