In recent years, wave energy has gained popularity among marine researchers for its
sustainability, cleanliness, high energy density and wide distribution. As one of the most …

Multi-view deep matrix factorization has recently gained popularity for extracting high-quality
representations from multi-view data to improve the processing performance of multi-view …

Semi-supervised symmetric nonnegative matrix factorization (SNMF) has been extensively
utilized in both linear and nonlinear data clustering tasks. However, the current SNMF …

Nonnegative matrix factorization (NMF) integrating label propagation (LP) algorithm can
enhance the discrimination ability of low-dimensional representations. However, traditional …

Graph regularized nonnegative matrix factorization (GNMF) has been widely used in multi-
view clustering tasks due to its good clustering properties. However, it uses a simple graph …

Deep multiview clustering provides an efficient way to analyze the data consisting of multiple
modalities and features. Recently, the autoencoder (AE)-based deep multiview clustering …

The fusion of hyperspectral and multispectral images presents a challenge as it involves
blending a low-resolution hyperspectral image (HSI) with a corresponding multispectral …

The design of effective multi-view clustering algorithms has recently garnered significant
research attention. In this paper, we develop a robust multi-view clustering via collaborative …

A solution based on the Internet of Things and deep learning (DL) is proposed to better
address the issues of personalized educational resource recommendation and student …

Although graph neural networks (GNNs) can extract the latent relationship-level knowledge
among the graph nodes and have achieved excellent performance in unsupervised …