Autoencoders and their applications in machine learning: a survey
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 …
ability to learn data features and act as a dimensionality reduction method. With rapid …
[HTML][HTML] An outliers detection and elimination framework in classification task of data mining
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 …
considerable impact on the output. Therefore, removing or resolving it before the analysis is …
Generative adversarial nets for unsupervised outlier detection
X Du, J Chen, J Yu, S Li, Q Tan - Expert Systems with Applications, 2024 - Elsevier
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 …
research area for decades due to its wide range of applications in various fields. Many well …
A graph neural network-based bearing fault detection method
L **, financial
transactions, communication networks, and so on. However, most existing works trying to …
transactions, communication networks, and so on. However, most existing works trying to …
Deep autoencoder architecture with outliers for temporal attributed network embedding
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 …
representation for each node in each snapshot of a temporal network, which can be capable …