Results 21 to 30 of about 15,373 (263)
DAO-CP: Data-Adaptive Online CP decomposition for tensor stream
PLoS ONE, 2022 How can we accurately and efficiently decompose a tensor stream? Tensor decomposition is a crucial task in a wide range of applications and plays a significant role in latent feature extraction and estimation of unobserved entries of data. The problem of
Sangjun Son +3 more
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A video watermark algorithm based on tensor decomposition
Mathematical Biosciences and Engineering, 2019 Since most of the previous video watermark algorithms regard a video as a series of consecutive images, the embedding and extraction of watermark are performed on these images, and the correlation and redundancy among frames of a video are not considered.
Shanqing Zhang +4 more
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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 +2 more
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IEEE Access, 2018 Reliable detection and recovery of a microseismic event in large volume of passive monitoring data is usually a challenging task due to the low signal-to-noise ratio environment.
Naveed Iqbal +5 more
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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 +2 more
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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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Mathematical Biosciences and Engineering, 2019 For discovery of new usage of drugs, the function type of their target genes plays an important role, and the hypothesis of "Antagonist-GOF" and "Agonist-LOF" has laid a solid foundation for supporting drug repurposing.
Kaiyin Zhou +7 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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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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