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Low-Rank Tensor Decomposition
Infrared small target detection is a research hotspot in computer vision technology that plays an important role in infrared early warning systems....
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Riemannian preconditioned algorithms for tensor completion via tensor ring decomposition
We propose Riemannian preconditioned algorithms for the tensor completion problem via tensor ring decomposition. A new Riemannian metric is developed...
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Uniform decomposition of velocity gradient tensor
In this paper, the principal decomposition of the velocity gradient tensor [∇
v ] is discussed in 3 cases based on the discriminant ∆: ∆ < 0 with 1... -
Tensor Decomposition
Tensor decompositions provide a powerful platform for dimensionality reduction, which is the fundamental of high-dimensional data analysis. They can...
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Bayesian Tensor Decomposition for Signal Processing and Machine Learning Modeling, Tuning-Free Algorithms, and Applications
This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to...
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SVD-based algorithms for fully-connected tensor network decomposition
The popular fully-connected tensor network (FCTN) decomposition has achieved successful applications in many fields. A standard method to this...
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Doubly Accelerated Proximal Gradient for Nonnegative Tensor Decomposition
The accelerated proximal gradient (APG) is a classical algorithm for nonnegative tensor decomposition. The APG employs variable extrapolation to...
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Tensor Decomposition-assisted Multiview Subgroup Analysis
To learn the subgroup structure generated by multidimensional interaction, we propose a novel multiview subgroup integration technique based on...
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A random sampling algorithm for fully-connected tensor network decomposition with applications
Fully-connected tensor network (FCTN) decomposition is a generalization of the popular tensor train and tensor ring decompositions and has been...
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Towards efficient and accurate approximation: tensor decomposition based on randomized block Krylov iteration
Tensor decomposition methods are inefficient when dealing with low-rank approximation of large-scale data. Randomized tensor decomposition has...
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Deep Convolutional Neural Network Compression Method: Tensor Ring Decomposition with Variational Bayesian Approach
Due to deep neural networks (DNNs) a large number of parameters, DNNs increase the demand for computing and storage during training, reasoning and...
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Spatial–temporal regularized tensor decomposition method for traffic speed data imputation
Data missing is very common in the spatial–temporal traffic data collected by various detectors, and how to accurately impute the missing values is...
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Tensor Decomposition: Basics, Algorithms, and Recent Advances
In this chapter, we will first introduce the preliminaries on tensors, including terminologies and the associated notations, related multi-linear...
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Faster quantum state decomposition with Tucker tensor approximation
Researchers have put a lot of effort into reducing the gap between current quantum processing units (QPU) capabilities and their potential supremacy....
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Provable Stochastic Algorithm for Large-Scale Fully-Connected Tensor Network Decomposition
The fully-connected tensor network (FCTN) decomposition is an emerging method for processing and analyzing higher-order tensors. For an N th-order...
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BTD-RF: 3D scene reconstruction using block-term tensor decomposition
The Neural Radiance Field (NeRF) exhibits excellent performance for view synthesis tasks, but it requires a large amount of memory and model...
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Multiomics-Based Tensor Decomposition for Characterizing Breast Cancer Heterogeneity
Breast cancer is heterogeneous and consists of intrinsic components with various alterations. Combining multiple genomic sources to identify the...
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SWoTTeD: an extension of tensor decomposition to temporal phenoty**
Tensor decomposition has recently been gaining attention in the machine learning community for the analysis of individual traces, such as Electronic...
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GNTD: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations
Spatially-resolved RNA profiling has now been widely used to understand cells’ structural organizations and functional roles in tissues, yet it is...
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Image inpainting based on tensor ring decomposition with generative adversarial network
Image inpainting is a fundamental task in the field of computer vision. However, there are three major challenges associated with this technique: (1)...
