A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data
Joint Authors
Song, Hongchao
Jiang, Zhuqing
Men, Aidong
Yang, Bo
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-15
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data.
Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier.
In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble k-nearest neighbor graphs- (K-NNG-) based anomaly detector.
Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace.
Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset.
The final prediction is made by all the anomaly detectors.
The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.
American Psychological Association (APA)
Song, Hongchao& Jiang, Zhuqing& Men, Aidong& Yang, Bo. 2017. A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1141174
Modern Language Association (MLA)
Song, Hongchao…[et al.]. A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1141174
American Medical Association (AMA)
Song, Hongchao& Jiang, Zhuqing& Men, Aidong& Yang, Bo. A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1141174
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141174