Search
Search Results
-
Low-rank GAT: toward robust quantification of neighborhood influence
Graph attention networks stack self-attention layers to compute the neighbor-specific weights. Due to inherent noise and artificially correlated...
-
Nonnegative low-rank tensor completion method for spatiotemporal traffic data
Although tensor completion theory performs well with high data missing rates, a lack of attention is encountered at the level of data completion...
-
Robust multi-view low-rank embedding clustering
Significant improvements of multi-view subspace clustering have emerged in recent years. However, multi-view data are often lying on high-dimensional...
-
Sensitivity of low-rank matrix recovery
We characterize the first-order sensitivity of approximately recovering a low-rank matrix from linear measurements, a standard problem in compressed...
-
A novel detail injection framework using latent low-rank decomposition for multispectral pan-sharpening
A novel framework for pansharpening based on Latent Low-Rank Representation theory, called Detail injection using Latent Low-Rank decomposition based...
-
Low-Rank Tensor Recovery
During data acquisition and transmission, some entries of data are missing, which will degrade the performance of subsequent data processing. Missing...
-
Non-negative low-rank approximations for multi-dimensional arrays on statistical manifold
Although low-rank approximation of multi-dimensional arrays has been widely discussed in linear algebra, its statistical properties remain unclear....
-
Low-rank tensor structure preservation in fractional operators by means of exponential sums
The use of fractional differential equations is a key tool in modeling non-local phenomena. Often, an efficient scheme for solving a linear system...
-
Stacked Bin Convolutional Neural Networks based Sparse Low-Rank Regressor: Robust, Scalable and Novel Model for Memorability Prediction of Videos
Driven by Internet technology, the explosion of video content is growing exponentially. Hence, the need for video analysis and interpretation by...
-
A general multi-factor norm based low-rank tensor completion framework
Low-rank tensor completion aims to recover the missing entries of the tensor from its partially observed data by using the low-rank property of the...
-
Dynamical low-rank approximation of the Vlasov–Poisson equation with piecewise linear spatial boundary
Dynamical low-rank approximation (DLRA) for the numerical simulation of Vlasov–Poisson equations is based on separation of space and velocity...
-
Linear Stochastic Processes on Networks and Low Rank Graph Limits
The modelling of stochastic linear systems in large complex networks is intractable computationally and may be impossible due to data-collection...
-
Low-Rank Approximation of Matrices for PMI-Based Word Embeddings
We perform an empirical evaluation of several methods of low-rank approximation in the problem of obtaining PMI-based word embeddings. All word...
-
Sparse low-redundancy multi-label feature selection with constrained laplacian rank
As one of the crucial methods for data dimensionality reduction, multi-label feature selection aims to eliminate irrelevant and redundant features...
-
Low-rank nonnegative tensor approximation via alternating projections and sketching
We show how to construct nonnegative low-rank approximations of nonnegative tensors in Tucker and tensor train formats. We use alternating...
-
Multi-view spectral clustering based on adaptive neighbor learning and low-rank tensor decomposition
Aiming at the problem that traditional multi-view clustering algorithms only focus on shared information in multi-views and ignore the unique...
-
A novel low-light enhancement via fractional-order and low-rank regularized retinex model
Most of existing low-light image enhancement approaches either fail to consider fine parts of the image or fail to consider intensive noise. To...
-
Low-rank tensor completion with spatial-spectral consistency for hyperspectral image restoration
Hyperspectral image (HSI) restoration has been widely used to improve the quality of HSI. HSIs are often impacted by various degradations, such as...
-
Nonnegative low rank tensor approximations with multidimensional image applications
The main aim of this paper is to develop a new algorithm for computing a nonnegative low rank tensor approximation for nonnegative tensors that arise...
-
Low tensor-ring rank completion: parallel matrix factorization with smoothness on latent space
In recent years, tensor ring (TR) decomposition has drawn a lot of attention and was successfully applied to tensor completion problem, due to its...
