The simultaneous minimization of the reconstruction and classification error is a hard non
convex problem, especially when a non-linear mapping is utilized. To overcome this …

Researchers typically investigate neural network representations by examining activation
outputs for one or more layers of a network. Here, we investigate the potential for ReLU …

The combination of clustering with Deep Learning has gained much attention in recent
years. Unsupervised neural networks like autoencoders can autonomously learn the …

Spectral clustering is a well-known clustering algorithm for unsupervised learning, and its
improved algorithms have been successfully adapted for many real-world applications …

Spectral clustering is one of the most popular modern clustering algorithms. It is easy to
implement, can be solved efficiently, and very often outperforms other traditional clustering …

The recognition and clustering of deformation modes are key to constructing impact
deformation constraints for thin-walled structures. This paper transforms the clustering and …

Detecting clusters over attributed graphs is a fundamental task in the graph analysis field.
The goal is to partition nodes into dense clusters based on both their attributes and …

Document embedding methods for clustering using deep neural networks have been
proposed recently. However, the existing deep neural network-based document embedding …

Deep spectral clustering is an advanced unsupervised deep learning method with
widespread applications. However, when applied to large-scale datasets, it may confront …

Link prediction models endeavor to understand the distribution of links within graphs and
forecast the presence of potential links. With the advancements in deep learning, prevailing …