首页|Combining KNN with AutoEncoder for Outlier Detection
Combining KNN with AutoEncoder for Outlier Detection
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万方数据
维普
K-nearest neighbor(KNN)is one of the most fundamental methods for unsupervised outlier detection be-cause of its various advantages,e.g.,ease of use and relatively high accuracy.Currently,most data analytic tasks need to deal with high-dimensional data,and the KNN-based methods often fail due to"the curse of dimensionality".AutoEn-coder-based methods have recently been introduced to use reconstruction errors for outlier detection on high-dimensional data,but the direct use of AutoEncoder typically does not preserve the data proximity relationships well for outlier detec-tion.In this study,we propose to combine KNN with AutoEncoder for outlier detection.First,we propose the Nearest Neighbor AutoEncoder(NNAE)by persevering the original data proximity in a much lower dimension that is more suit-able for performing KNN.Second,we propose the K-nearest reconstruction neighbors(KNRNs)by incorporating the re-construction errors of NNAE with the K-distances of KNN to detect outliers.Third,we develop a method to automatical-ly choose better parameters for optimizing the structure of NNAE.Finally,using five real-world datasets,we experimen-tally show that our proposed approach NNAE+KNRN is much better than existing methods,i.e.,KNN,Isolation Forest,a traditional AutoEncoder using reconstruction errors(AutoEncoder-RE),and Robust AutoEncoder.
万方数据
维普
