Outlier detection is of great significance in data processing.To solve the problem of large data volume and exploding dimensionality,this paper proposes a deep autoencoder support vector machine(AE-OVSVM)model.The model first uses a deep autoencoder network to reduce the dimensionality of the input data for feature representation,and then uses OC-SVM to predict outliers.Finally,9 algorithms including Isolation Forest,OC-SVM,PCA KMeans,PCA-GMM(TN=0),DBSCAN,LOF,DAGMM,VAEGMM,and AE ocsvm were used to process the same set of data,verifying that the proposed method outperforms other models.
维普
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