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Crowd flows prediction is an important problem for traffic management and public safety. Graph Convolutional Network (GCN), known for its ability to effectively capture and utilize topological information, has demonstrated significant advancements in ...

This paper derives the CUR-type factorization for tensors in the Tucker format based on a new variant of the discrete empirical interpolation method known as L-DEIM. This novel sampling technique allows us to construct an efficient algorithm for ...

Dynamic quality of service (QoS) data contain rich temporal patterns of user-service interactions, which are vital for better understanding user behaviors and service conditions. Canonical polyadic (CP)-based latent factorization model has proven ...

• A model utilizes a tucker decomposition paradigm for accurate QoS prediction.
• Incorporation of Tucker decomposition (TD) helps to extract the feature accurately.
• Decoupled ranks allow accuracy boosting with optimal ranks for a ...

In this paper, we introduce a unified low-rank and sparse enhanced Tucker decomposition model for tensor completion. Our model possesses a sparse regularization term to promote a sparse core of the Tucker decomposition, which is beneficial for ...

Non-negative Tucker decomposition (NTD) is a powerful tool for data representation to capture rich internal structure information from non-negative high-dimensional tensor data. Arguing that NTD methods often give global-like information, graph ...

Knowledge Graph Embedding (KGE) translates entities and relations of knowledge graphs (KGs) into a low-dimensional vector space, enabling an efficient way of predicting missing facts. Generally, KGE models are trained with positive and negative ...

We present ATC, a C++ library for advanced Tucker-based lossy compression of dense multidimensional numerical data in a shared-memory parallel setting, based on the sequentially truncated higher-order singular value decomposition (ST-HOSVD) and bit plane ...

Low-rank approximation of tensors has been widely used in high-dimensional data analysis. It usually involves singular value decomposition (SVD) of large-scale matrices with high computational complexity. Sketching is an effective data compression ...

Optimization problems with tensor variables are widely used in statistics, machine learning, pattern recognition, signal processing, computer vision, etc. Among these applications, the low-rankness of tensors is an intrinsic property that can help ...

Wilson’s Disease (WD) is a rare, autosomal recessive disorder caused by excessive accumulation of Copper (Cu) in various human organs such as the liver, brain, and eyes. Accurate WD diagnosis is challenging because of: (1) subtle intensity ...

Given a dense tensor, how can we efficiently discover hidden relations and patterns in static and online streaming settings? Tucker decomposition is a fundamental tool to analyze multidimensional arrays in the form of tensors. However, existing Tucker ...

This work represents a study of the capabilities of an approach for lossy video compression based on the tensor train and Tucker tensor formats. The TTSVD and st-HOSVD algorithms are used for video compression, which are represented as ...

This paper is concerned with the low Tucker-rank tensor completion problem, which is about reconstructing a tensor Τ ∈ ℝ^n×n×n of low multilinear rank from partially observed entries. Riemannian optimization algorithms are a class of efficient methods for ...

Convolutional neural networks (CNNs) usually come with numerous parameters and thus are not convenient for some situations, such as when the storage space is limited. Low-rank decomposition is one effective way for network compression ...

The tensor CUR decomposition in the Tucker format is a special case of Tucker decomposition with a low multilinear rank, where factor matrices are obtained by selecting some columns from the mode-n unfolding of the tensor. We perform a thorough ...

Dimensionality Reduction (DR) is a significant subject which have aroused extensive attention of researchers. In this paper, a novel method is proposed to reduce the dimensionality of tensor data based on orthogonal Tucker decomposition model and ...

Super-resolution is an important way to improve the spatial resolution of Hyperspectral images (HSIs). In this paper, we propose a super-resolution method based on low-rank tensor Tucker Decomposition and weighted 3D total variation (TV) for HSIs. ...

Deep convolutional neural networks have achieved state of the art results in many image classification tasks. However, the large amount of parameters of the network limit its deployment to storage space limited situations. Low-rank decomposition ...

• Lightfield representation, coding and streaming schemes using a stack of focal images.

Computational multi-view displays involving light fields are a fast emerging choice for 3D presentation of real-world scenes. Tensor autostereoscopic glasses-free displays use just few light attenuating layers in front of a backlight ...

Aiming at the problem of blind estimation of the spreading code of multi-antenna Direct-Spread CDMA Long-Code (DS-CDMA LC) signals with poor interpolation effect, the idea of segmentation is used to construct the third-order tensor of the received ...