Autoencoders have become a hot researched topic in unsupervised learning due to their
ability to learn data features and act as a dimensionality reduction method. With rapid …

An outlier is a datum that is far from other data points in which it occurs. It can have a
considerable impact on the output. Therefore, removing or resolving it before the analysis is …

Outlier detection, also known as anomaly detection, has been a persistent and active
research area for decades due to its wide range of applications in various fields. Many well …

Bearings are very important components in mechanical equipment, and detecting bearing
failures helps ensure healthy operation of mechanical equipment and can prevent …

Unsupervised Learning is widely used approach for outlier detection because non-
availability of training dataset in various domains (especially, in evolving domains) …

Open-set recognition aims to deal with unknown classes that do not exist in the training
phase. The key is to learn effective latent feature representations for classifying the already …

Outlier detection is the discovery of some objects that are significantly different from many
objects in data, and it is widely used in important fields. Most existing methods are based on …

Anomaly detection on attributed networks is widely used in online shopping, financial
transactions, communication networks, and so on. However, most existing works trying to …

Temporal attributed network embedding aspires to learn a low-dimensional vector
representation for each node in each snapshot of a temporal network, which can be capable …

Nowadays, as a crucial component of intelligent transportation systems, traffic flow
prediction has received extensive concern. However, most of the existing studies extracted …