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Low-rank tensor representation (LRTR) has become a significant method for achieving improved multi-view clustering (MVC) performance. Generally, most LRTR methods impose a tensor low-rank constraint (TLRC) on a tensor, which is spliced by the ...

• Our model integrates local smoothness and low-rank tensor representation for clustering.
• Our model applies TV norm to explore the local smoothness and denoise.
• Our model is the first to leverage both low-rank and local smoothness.

The Singular Value Decomposition (SVD) of matrices is a widely used tool in scientific computing. In many applications of machine learning, data analysis, signal and image processing, the large datasets are structured into tensors, for which ...

The extremal problem of two types of high order spectra for graphs are considered, which are called r-adjacency spectrum and t-clique spectrum, respectively. In this paper, we obtain the maximum r-adjacency spectral radius of a K r + 1 minor-free ...

Multiple-input multiple-output (MIMO) systems employing multicarrier modulation (MCM), including flexible MCM that unifies several MCM schemes, have been well studied recently also in their tensor-based formulation. The latter naturally allows ...

Linear filtering methods are well-known and have been successfully applied to system identification and equalization problems. However, when high-dimensional systems are modeled, these methods often perform unsatisfactorily due to their slow ...

The k-tensor Ising model is a multivariate exponential family on a p-dimensional binary hypercube for modeling dependent binary data, where the sufficient statistic consists of all k-fold products of the observations, and the parameter is an ...

Multi-view Spectral Clustering (MvSC) partitions data into clusters according to multiple views for higher performance. However, most existing works overlook model interpretability and involve iterative and alternating updates on parameters with ...

• A new method for MvSC is proposed via a novel conjugate feature duality.
• With our duality, a common latent space can be imposed for different data sources.
• A tensor-based modeling is derived for higher-order couplings and ...

In this paper, we investigate bounds of solution set of the polynomial complementarity problem. When a polynomial complementarity problem has a solution, we propose a lower bound of solution norm by entries of coefficient tensors of the ...${}_{\mathrm{}}$

In this paper, we propose a novel rank-adaptive higher-order orthogonal iteration (RA-HOOI) algorithm to solve the fixed-accuracy low multilinear-rank approximation of tensors. On the one hand, RA-HOOI relies on a greedy strategy to expand the ...

Molecular dynamics(MD) simulations are essential for molecular structure optimization, drug-drug interactions, and other fields of drug discovery by simulating the motion of microscopic particles to calculate their macroscopic properties (e.g., ...

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Coprime arrays are able to utilize two subarrays to construct a difference coarray to obtain more degrees of freedom (DOF). A novel frequency diverse coprime array (FDCA) is developed for estimating the angle-range for FDA-multiple input multiple output (...

In this paper, we first study the equivalence between the third order tensor linear complementarity problem under the framework of t-product and the least squares problem under the t-product with nonnegative constraints, and based on their ...$\mathrm{}$$\mathrm{}$$\mathrm{}$

The method based on non-local self-similarity patches has been widely applied in image denoising. For a given image patch, there are similar image patches at neighbor windows in the image. Traditionally, similar patches are vectorized and then ...

• Firstly, we use HOSVD on the tensor composed of similar blocks to obtain its core tensor.
• We establish a hybrid model of tensor sparse representation and total variation regularization.
• The primal-dual algorithm is taken to ...

The automatic price evaluation is an important and challenging issue for the used mobile phone recycling. However, due to complicated relationships between attributes and price as well as insufficient sample data, current approaches cannot ...

• Mutual information is used to select the most relevant attributes.
• Boxplot is used to select suitable price samples.
• Tensor model is used to establish relationships between attributes and prices.
• CP decomposition based ...

The massive amount of data and the problem of processing them is one of the main challenges of the digital age, and the development of artificial intelligence and machine learning can be useful in solving this problem. Using deep neural networks ...

Frequency diversity array - Multiple input multiple output (FDA-MIMO) radar has a two-dimensional angle-range dependence due to the existence of certain frequency offset between transmitting elements. For obtaining angle-range estimation, a fast ...

Microscopic hyperspectral images has the advantage of containing rich spatial and spectral information. However, the large number of spectral bands provides a significant amount of spectral features, but also leads to data redundancy and noise, ...

• A tensor theory and semi-supervised idea inspired EDLCPP DR method is proposed.
• An attention mechanism is suggested to enhance the efficiency of the DR.
• Select and fully leverage the sample with high discriminability.
• EDLCPP ...

• We conduct a systematic theoretical study concerning optimality for tensor discriminant analysis (TDA), with results applicable to tensors with arbitrary orders and ultra-high dimensions.
• We provide the minimax lower bounds for the ...

Tensor discriminant analysis is an important topic in tensor data analysis. However, given the many proposals for tensor discriminant analysis methods, there lacks a systematic theoretical study, especially concerning optimality. We fill this gap ...

Multivariable time-series prediction based on the denoising diffusion probabilistic model (DDPM) highlights a major challenge in improving prediction robustness and ensuring relatively low computing costs. This paper proposes a novel hierarchical ...

The tensor t-product is a powerful tool for the analysis of and computation with third-order tensors. This paper discusses properties and the computation of eigentubes and eigenslices of third-order tensors under the t-product; the ...