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Laplacian regularized low-rank sparse representation transfer learning
In unsupervised transfer learning, it is extremely valuable to effectively extract knowledge from the vast amount of untagged data that exists by...
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An Image Denoising Algorithm Combining Global Clustering and Low-Rank Theory
In recent years, image denoising algorithms based on non-local image prior information have been extensively researched. At present, most similar...
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Efficient low-rank multi-component fusion with component-specific factors in image-recipe retrieval
Image-Recipe retrieval is the task of retrieving closely related recipes from a collection given a food image and vice versa. The modality gap...
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Low-rank tensor reconstruction of concentrated densities with application to Bayesian inversion
This paper presents a novel method for the accurate functional approximation of possibly highly concentrated probability densities. It is based on...
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Kernel embedding of measures and low-rank approximation of integral operators
We describe a natural coisometry from the Hilbert space of all Hilbert-Schmidt operators on a separable reproducing kernel Hilbert space
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Combustion and Self-Desulfurization Characterization of Blended Low-Rank Coals for Improved Resource Utilization in Fluidized Bed Boilers
The co-combustion of low-rank coals through fluidized bed boiler (CFB) is an effective approach to enhance the level of resource utilization. To...
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Multiscale pore distribution and evolution characteristic of medium/low-rank coal
Some medium rank coal samples (from Dongshan coal mine with the depth of 840 m) and low-rank coal samples (from Yimin coal mine with the depth of...
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A multiple kinds of information extraction method for multi-view low-rank subspace clustering
Recently, multi-view subspace clustering has attracted intensive attentions due to the remarkable clustering performance by extracting abundant...
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An Adaptive Dynamical Low Rank Method for the Nonlinear Boltzmann Equation
Efficient and accurate numerical approximation of the full Boltzmann equation has been a longstanding challenging problem in kinetic theory. This is...
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Research on Image Denoising Algorithm Based on Edge Enhancement Sparse Transform and Low Rank
Focused on the issue that detailed information of edge is easily lost in the process of image denoising, the image denoising algorithm based on edge...
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Multi-scale low-rank approximation method for image denoising
Nonlocal self-similarity (NSS) as a prior has been widely used in image denoising techniques, such as block-matching and 3-D filtering (BM3D) and...
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Low-rank approximation to entangled multipartite quantum systems
Qualifying the entanglement of a mixed multipartite state by gauging its distance to the nearest separable state of a fixed rank is a challenging but...
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A low-rank deep image prior reconstruction for free-breathing ungated spiral functional CMR at 0.55 T and 1.5 T
ObjectiveThis study combines a deep image prior with low-rank subspace modeling to enable real-time (free-breathing and ungated) functional cardiac...
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Fast hypergraph regularized nonnegative tensor ring decomposition based on low-rank approximation
Tensor ring (TR) decomposition is a highly effective tool for obtaining the low-rank character of multi-way data. Recently, nonnegative tensor ring...
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ARGLRR: An Adjusted Random Walk Graph Regularization Sparse Low-Rank Representation Method for Single-Cell RNA-Sequencing Data Clustering
Researchers may now explore biological concerns at the cell level because of the advancement of single-cell transcriptome sequencing technologies....
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Robust semi non-negative low-rank graph embedding algorithm via the L21 norm
The non-negative matrix factorization (NMF) method is applied in many fields, including pattern recognition, visual analysis, and biomedicine....
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QR Factorization of Block Low-Rank Matrices on Multi-instance GPU
The QR factorization, which is a fundamental operation in linear algebra, is used extensively in scientific simulations. The acceleration and memory...
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Multiple kernel-based anchor graph coupled low-rank tensor learning for incomplete multi-view clustering
Incomplete Multi-View Clustering (IMVC) attempts to give an optimal clustering solution for incomplete multi-view data that suffer from missing...
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Low-Rank Tensor Tucker Decomposition for Hyperspectral Images Super-Resolution
Super-resolution is an important way to improve the spatial resolution of Hyperspectral images (HSIs). In this paper, we propose a super-resolution...
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Low-Rank Ensemble Methods
This chapter willError covariance matrix introduce another approximation where we represent all state error covariances using a finite ensemble of...