Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection

Dimensionality reduction is a crucial first step for many unsupervised learning tasks including anomaly detection and clustering. Autoencoder is a popular mechanism to accomplish dimensionality reduction. In order to make dimensionality reduction effective for high-dimensional data embedding nonline...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 44; číslo 8; s. 4110 - 4124
Hlavní autori:	Ahmed, Imtiaz, Galoppo, Travis, Hu, Xia, Ding, Yu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Anomalies Anomaly detection Autoencoder Clustering Cognitive tasks Data points Datasets Decoding Dimensionality reduction Embedding Euclidean geometry Generative adversarial networks Graph theory Laplace equations Manifolds Measurement minimum spanning tree Neural networks nonlinear embedding Performance enhancement Reduction unsupervised learning
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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