文章基本信息

标题：A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data
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作者：Hongchao Song ; Zhuqing Jiang ; Aidong Men 等
期刊名称：Computational Intelligence and Neuroscience
印刷版ISSN：1687-5265
电子版ISSN：1687-5273
出版年度：2017
卷号：2017
DOI：10.1155/2017/8501683
出版社：Hindawi Publishing Corporation
摘要：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 -nearest neighbor graphs- (-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.