Unsupervised feature selection attempts to select a small number of discriminative features
from original high-dimensional data and preserve the intrinsic data structure without using …

Contrastive learning has recently attracted plenty of attention in deep graph clustering due to
its promising performance. However, complicated data augmentations and time-consuming …

The wide application of smart devices enables the availability of multimodal data, which can
be utilized in many tasks. In the field of multimodal sentiment analysis, most previous works …

Attributed graph (AG) clustering is a fundamental, yet challenging, task for studying
underlying network structures. Recently, a variety of graph representation learning models …

In this paper, the Red Deer algorithm (RDA), a recent population-based meta-heuristic
algorithm, is thoroughly reviewed. The RD algorithm combines the survival of the fittest …

Unsupervised person re-identification (Re-ID) aims at finding the most informative features
from unlabeled person datasets. Some recent approaches adopted camera-aware …

The new architecture TransUNet, which combines convolutional neural networks (CNNs)
and transformers, has displayed competitive performance in medical image segmentation. In …

Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent
manufacturing systems. Deep learning-based methods have been recently developed for …

Graph-level representations are critical in various real-world applications, such as predicting
the properties of molecules. However, in practice, precise graph annotations are generally …

Graph convolutional network (GCN) outputs powerful representation by considering the
structure information of the data to conduct representation learning, but its robustness is …