Deep convolutional neural networks (CNNs) have attracted great attention in the field of
image denoising. However, there are two drawbacks:(1) it is very difficult to train a deeper …

Abstract The Artificial Intelligence Internet of Things (AIoT) is an emerging concept aiming to
perceive, understand and connect the 'intelligent things' to make the intercommunication of …

In real-world applications, it is often that the collected multi-view data are incomplete, ie,
some views of samples are absent. Existing clustering methods for incomplete multi-view …

The labeled monitoring data collected from the electromechanical system is limited in the
real industries; traditional intelligent fault diagnosis methods cannot achieve satisfactory …

Generative Adversarial Networks (GANs) have been widely applied in different scenarios
thanks to the development of deep neural networks. The original GAN was proposed based …

Multi-view clustering aims to partition data collected from diverse sources based on the
assumption that all views are complete. However, such prior assumption is hardly satisfied …

Multiple kernel learning (MKL) is generally recognized to perform better than single kernel
learning (SKL) in handling nonlinear clustering problem, largely thanks to MKL avoids …

Multi-view graph embedding is aimed at learning low-dimensional representations of nodes
that capture various relationships in a multi-view network, where each view represents a …

Multi-view clustering is widely used to improve clustering performance. Recently, the
subspace clustering tensor learning method based on Markov chain is a crucial branch of …

Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep
learning technique has received a great deal of attention and has been widely applied in the …