Outlier Detection Based on Stacked Autoencoder and Gaussian Mixture Model
Jing Li, Pengbo Lv, Huijun Li, Wanghu Chen · 2022 IEEE International Conference on Big Data (Big Data) · 2022
The outlier detection of high-dimensional data is still of challenge. The performance of existing unsupervised approaches will be affected with the increase of outliers in a dataset. The stacked autoencoder and GMM are introduced to the detection of outliers, and an approach termed SAGMM is proposed. The stacked autoencoder can reduce the reconstruction error of observations, and the GMM determines the outliers based on the mixture distributions of observations obtained in model training. Experiments on public datasets show that the proposed approach SAGMM outperforms the similar approaches in precision, and has a good balance between the precision and recall rate, since it improves the F1 scores compared with them.