Abstract: Unsupervised anomaly detection on high-dimensional data is one of the most significant challenges in machine learning.Although previous approaches based on single deep auto-encoder and density estimations have made significant progress,they generate low-dimensional representations as they use only a single deep auto-encoder,indicating that there is insufficient information to perform the subsequent density estimation task.To address the above challenge,a mixed auto-encoding gaussian mixture model (MAGMM) is proposed in this paper.MAGMM substitutes a single deep auto-encoder with a mixture of auto-encoders to generate concatenated low-dimensional representations,so that it can preserve key information from a specific cluster of the input sample.In addition,it utilizes an allocation network to constrain the mixture of auto-encoders,so that each sample can be assigned to a dominant auto-encoder.With the above mechanisms,MAGMM avoids from trapping into local optima and reduces the recons-truction errors,which can facilitate completing the density estimation tasks and improve the accuracy of high-dimensional data anomaly detection.Experimental results show that the proposed method performs better than DAGMM and achieves up to 29% improvement based on the standard F1 score.

Key words: Data mining, Density estimation, Dimensionality reduction, Gaussian mixture model, Unsupervised anomaly detection