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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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 8; pp. 4110 - 4124
Main Authors:	Ahmed, Imtiaz, Galoppo, Travis, Hu, Xia, Ding, Yu
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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