Matrix completion, the problem of completing missing entries in a data matrix with low-dimensional structure (such as rank), has seen many fruitful approaches and analyses. Tensor completion is the tensor analog that attempts to impute missing tensor...
Robust tensor completion aims to recover a clean tensor from noisy and incomplete observations, where the observed tensor is corrupted by Gaussian noise and sparse noise simultaneously. Existing methods only exploit one or two priors out of global te...
In this paper, we proposed an image inpainting algorithm, including an interpolation step and a non-local tensor completion step based on a weighted tensor nuclear norm. Specifically, the proposed algorithm adopts the triangular based linear interpol...
As a promising data analysis technique, sparse modeling has gained widespread traction in the field of image processing, particularly for image recovery. The matrix rank, served as a measure of data sparsity, quantifies the sparsity within the Kronec...
Tensor completion is a fundamental tool to estimate unknown information from observed data, which is widely used in many areas, including image and video recovery, traffic data completion and the multi-input multi-output problems in information theor...
Image completion, which falls to a special type of inverse problems, is an important but challenging task. The difficulties lie in that (i) the datasets usually appear to be multi-dimensional; (ii) the unavailable or corrupted data entries are random...
Multi-channel audio signals provide a better auditory sensation to the audience. However, missing data may occur in the collection, transmission, compression, or other processes of audio signals, resulting in audio quality degradation and affecting t...
Many tasks in computer vision suffer from missing values in tensor data, i.e., multi-way data array. The recently proposed tensor tubal nuclear norm (TNN) has shown superiority in imputing missing values in 3D visual data, like color images and video...
Existing tensor completion methods all require some hyperparameters. However, these hyperparameters determine the performance of each method, and it is difficult to tune them. In this paper, we propose a novel nonparametric tensor completion method,...
Tensor completion is an extension of matrix completion aimed at recovering a multiway data tensor by leveraging a given subset of its entries (observations) and the pattern of observation. The low-rank assumption is key in establishing a relationship...
Recovering incomplete high-dimensional data to create complete and valuable datasets is the main focus of tensor completion research, which lies at the intersection of mathematics and information science. Researchers typically apply various linear an...
Array sensor failure poses a serious challenge to robust direction-of-arrival (DOA) estimation in complicated environments. Although existing matrix completion methods can successfully recover the damaged signals of an impaired sensor array, they can...
Tensor completion is commonly formulated by minimizing a convex combination of nuclear norms of mode-wise unfolding matrices. Although effective, the non-negative weight constraint can limit the flexibility of mode balancing, especially when differen...
Supervisory Control And Data Acquisition (SCADA) systems currently monitor and collect a huge among of data from all kind of processes. Ideally, they must run without interruption, but in practice, some data may be lost due to a sensor failure or a c...
The issue of image completion has been developed considerably over the last two decades, and many computational strategies have been proposed to fill-in missing regions in an incomplete image. When the incomplete image contains many small-sized irreg...
Completing traffic data is a basic requirement for intelligent transportation systems. However, completing spatiotemporal traffic data poses a significant challenge, especially for high-dimensional data with complex missing mechanisms. Various comple...
In recent years, the tensor completion algorithm has played a vital part in the reconstruction of missing elements within high-dimensional remote sensing image data. Due to the difficulty of tensor rank computation, scholars have proposed many substi...
This paper conducts a rigorous analysis for the problem of robust tensor completion, which aims at recovering an unknown three-way tensor from incomplete observations corrupted by gross sparse outliers and small dense noises simultaneously due to var...
Many restoration methods use the low-rank constraint of high-dimensional image signals to recover corrupted images. These signals are usually represented by tensors, which can maintain their inherent relevance. The image of this simple tensor present...
Automotive radar pursues low cost and high performance, and especially hopes to improve the angular resolution under the condition of a limited number of multiple-input–multiple-output (MIMO) radar channels. Conventional time division multiplex...
With the rise of mobile edge computing (MEC), mobile services with the same or similar functions are gradually increasing. Usually, Quality of Service (QoS) has become an indicator to measure high-quality services. In the real MEC service invocation...
Accurate mapping of localization error distribution is essential for assessing passive sensor systems and guiding sensor placement. However, conventional analytical methods like the Geometrical Dilution of Precision (GDOP) rely on idealized error mod...
Clouds often contaminate remote sensing images, which leads to missing land feature information and subsequent application degradation. Low-rank tensor completion has shown great potential in the reconstruction of multi-temporal remote sensing images...
The rapid development of the low-altitude economy demands comprehensive electromagnetic spectrum awareness. However, constructing a comprehensive radio environment map (REM) in this scenario is challenging, as spectrum sensing data collected by unman...
Landslide displacement prediction is a challenging research task that can help to reduce the occurrence of landslide disasters. The frequent occurrence of extreme weather increases the probability of landslides, and the subsequent increase in the sup...
In this paper, we show that even-order Pascal tensors are positive-definite, and odd-order Pascal tensors are strongly completely positive. The significance of these is that our induction proof method also holds for some other families of completely...
The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in...
In the past decade, multi-view clustering has received a lot of attention due to the popularity of multi-view data. However, not all samples can be observed from every view due to some unavoidable factors, resulting in the incomplete multi-view clust...
The purpose of the present paper is to study the complete lifts of a QSNMC from an LP-Sasakian manifold to its tangent bundle. The lifts of the curvature tensor, Ricci tensor, projective Ricci tensor, and lifts of Einstein manifold endowed with QSNMC...
Tensor restoration finds applications in various fields, including data science, image processing, and machine learning, where the global low-rank property is a crucial prior. As the convex relaxation to the tensor rank function, the traditional tens...
Low-rank tensors have received more attention in hyperspectral image (HSI) recovery. Minimizing the tensor nuclear norm, as a low-rank approximation method, often leads to modeling bias. To achieve an unbiased approximation and improve the robustness...
This work contributes to the techniques used for SCADA (Supervisory Control and Data Acquisition) system data completion in databases containing historical water sensor signals from a water supplier company. Our approach addresses the data restoratio...
The objective of this paper is to explore the complete lifts of a quarter-symmetric metric connection from a Sasakian manifold to its tangent bundle. A relationship between the Riemannian connection and the quarter-symmetric metric connection from a...
To address the issue of missing position in flight trajectory data collected by Automatic Dependent Surveillance-Broadcast (ADS-B) systems, a flight trajectory tensor completion model based on truncated Schatten p-norm minimization is proposed. First...
To improve the accuracy of color image completion with missing entries, we present a recovery method based on generalized higher-order scalars. We extend the traditional second-order matrix model to a more comprehensive higher-order matrix equivalent...
With the continuous development of power systems, WAMS have become increasingly important for real-time system monitoring. As the core devices of WAMS, PMUs can provide synchronized, high-precision, and high-resolution measurements of power system st...
With the continuous expansion of the expressway network and the rapid growth of traffic demand, traditional single-source traffic detection data is limited in spatial–temporal coverage and accuracy, which can hardly support the refined operatio...
Reconstruction of 3D objects in various tomographic measurements is an important problem which can be naturally addressed within the mathematical framework of 3D tensors. In Optical Coherence Tomography, the reconstruction problem can be recast as a...
With the widespread application of multi-view data across various domains, multi-view unsupervised feature selection (MUFS) has achieved remarkable progress in both feature selection (FS) and missing-view completion. However, existing MUFS methods ty...
We prove various results in infinite-dimensional differential calculus that relate the differentiability properties of functions and associated operator-valued functions (e.g., differentials). The results are applied in two areas: (1) in the theory o...
In forestry data management and analysis, data integrity and analytical accuracy are of critical importance. However, existing techniques face a dual challenge: first, sensor failures, data transmission interruptions, and human errors lead to the pre...
As a fundamental information carrier in Industrial Internet of Things (IIoT), electromagnetic spectrum data presents critical challenges for efficient spectrum sensing and situational awareness in smart industrial cognitive radio systems. Addressing...
This work is devoted to the systematic study of the optical and magneto-optical properties of sputter deposited CuFe2O4 thin films in the photon energy region between 2 and 5 eV using spectroscopic ellipsometry and magneto-optical Kerr spectroscopy....
In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive...
Conventional total variation (TV) regularization methods based on Laplacian or fixed-scale Hyper-Laplacian priors impose uniform sparsity penalties on gradients. These uniform penalties fail to capture the heterogeneous sparsity characteristics acros...
In this study, an extension of the general method [G. Gaigalas, Z. Rudzikas, C. Froese Fischer, J. Phys. B, At. Mol. Phys. (1997). DOI: 10.1088/0953-4075/30/17/006] is described for finding algebraic expressions of the spin-angular parts of the reduc...
The main aim of the proposed paper is to investigate the lifts of Kenmotsu manifolds that admit NSNMC in the tangent bundle. We investigate several properties of the lifts of the curvature tensor, the conformal curvature tensor, and the conharmonic c...
The purpose of this study is to examine the complete lifts from the symmetric and concircular symmetric n-dimensional Lorentzian para-Sasakian manifolds (briefly,
Remote sensing satellite images in the optical domain often contain missing or misleading data due to overcast conditions or sensor malfunctioning, concealing potentially important information. In this paper, we apply expectation maximization (EM) Tu...
The fog radio access network (F-RAN) equipped with enhanced remote radio heads (eRRHs), which can pre-store some requested files in the edge cache and support mobile edge computing (MEC). To guarantee the quality-of-service (QoS) and energy efficienc...
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