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Graph-aware tensor factorization convolutional network for knowledge graph completion
Constructed by millions of triples, knowledge graph is a commonly used structured representation of information encoding both the entities of the...
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Hyperspectral Image Completion Via Tensor Factorization with a Bi-regularization Term
The main purpose of this article is to study a new model of low rank tensor completion. The goal is to predict missing values from a small set of...
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A Low-complexity Tensor Completion Scheme Combining Matrix Factorization and Smoothness
In this paper, the low-complexity tensor completion (LTC) scheme is proposed to improve the efficiency of low-rank tensor completion with competitive...
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Tensor Preliminaries
Tensors are multidimensional arrays generalized from vectors and matrices, which have a broad range of applications in various fields such as signal...
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Nonnegative Tensor Factorization based on Low-Rank Subspace for Facial Expression Recognition
Important progresses have been made in the field of artificial intelligence in recent years, and facial expression recognition (FER), which could...
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Multi-source Data-Based Deep Tensor Factorization for Predicting Disease-Associated miRNA Combinations
MicroRNAs (miRNAs) play a significant role in the occurrence and development of complex diseases. The regulatory level of multiple miRNAs is stronger...
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Coupled Tensor for Data Analysis
Tensor component analysis, which can reveal the underlying structure of multiway data and exploit the relationship among multiple modes, plays an...
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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...
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Tensor Completion-Based Data Imputation Framework for IoT-Based Underwater Sensor Network
In the IoT-based Underwater Sensor Network (IoT-USN), a set of underwater sensor nodes are deployed for monitoring of the marine ecosystems. These...
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Tensor Regression
Multiway data-related learning tasks pose a huge challenge to the traditional regression analysis techniques due to the existence of multidirectional...
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Tensor-Based Denoising on Multi-dimensional Diagnostic Signals of Rolling Bearing
PurposeTo improve fault diagnosis efficiency, a multidimensional denoising approach based on tensor decomposition is developed for solving...
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A faster tensor robust PCA via tensor factorization
Many kinds of real-world multi-way signal, like color images, videos, etc., are represented in tensor form and may often be corrupted by outliers. To...
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Fiber-Missing Tensor Completion for DOA Estimation with Sensor Failure
In this paper, we propose a fiber-missing tensor completion-based method for direction-of-arrival (DOA) estimation in sensor failure scenario, where...
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Tensor Dictionary Learning
Dictionary learning is one of classical data-driven ways for linear feature extraction, which finds wide applications in image recovery and...
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Entropy regularized fuzzy nonnegative matrix factorization for data clustering
Clustering high-dimensional data is very challenging due to the curse of dimensionality. To address this problem, low-rank matrix approximations are...
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Modeling user preference dynamics with coupled tensor factorization for social media recommendation
An essential problem in real-world recommender systems is that user preferences are not static and users are likely to change their preferences over...
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Tensor Subspace Cluster
As a typical unsupervised learning technique, subspace clustering learns the subspaces of data and assigns data into their respective subspaces,...
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Improvement of Incomplete Multiview Clustering by the Tensor Reconstruction of the Connectivity Graph
AbstractWith the development of data collection technologies, a significant volume of multiview data has appeared, and their clustering has become...
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Multiview nonnegative matrix factorization with dual HSIC constraints for clustering
To utilize multiple features for clustering, this paper proposes a novel method named as multiview nonnegative matrix factorization with dual HSIC...
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Tensor Decomposition
Tensor decompositions provide a powerful platform for dimensionality reduction, which is the fundamental of high-dimensional data analysis. They can...