Results 31 to 40 of about 16,615 (264)
Hankel Tensor Decompositions and Ranks [PDF]
SIAM Journal on Matrix Analysis and Applications, 2019 Hankel tensors are generalizations of Hankel matrices. This article studies the relations among various ranks of Hankel tensors. We give an algorithm that can compute the Vandermonde ranks and decompositions for all Hankel tensors. For a generic $n$-dimensional Hankel tensor of even order or order three, we prove that the the cp rank, symmetric rank ...
Jiawang Nie, Ke Ye
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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
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Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme
Applied SciencesEmploying 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 +4 more
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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 +3 more
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Efficient Tensor Decompositions
, 2020 This chapter studies the problem of decomposing a tensor into a sum of constituent rank one tensors. While tensor decompositions are very useful in designing learning algorithms and data analysis, they are NP-hard in the worst-case. We will see how to design efficient algorithms with provable guarantees under mild assumptions, and using beyond worst ...
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A Color Image Watermarking Based on Tensor Analysis
IEEE Access, 2018 Since most of the color image watermarking methods embed the watermark information in each channel or one channel of a color image, the redundant information of the color image cannot be sufficiently utilized, resulting in the poor ability to resist ...
Haiyong Xu +3 more
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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 +3 more
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Decomposition of Elasticity Tensor on Material Constants and Mesostructures of Metal Plates
CrystalsMost metal plates are orthorhombic aggregates of cubic crystallites. First, we discuss the representations of the stress tensor, the strain tensor, the elasticity tensor, and the rotation tensor under the Kelvin notation.
Genbao Liu +5 more
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L1-Norm Tucker Tensor Decomposition
IEEE Access, 2019 Tucker decomposition is a standard multi-way generalization of Principal-Component Analysis (PCA), appropriate for processing tensor data. Similar to PCA, Tucker decomposition has been shown to be sensitive against faulty data, due to its L2-norm-based ...
Dimitris G. Chachlakis +2 more
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TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed.
N. Li +6 more
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