Variational autoencoder-based outlier detection for high-dimensional data

Abstract

Analysis of high-dimensional data often suffers from the curse of dimensionality and the complicated correlation among dimensions. Dimension reduction methods often are used to alleviate these problems. Existing outlier detection methods based on dimension reduction usually only rely on reconstruction error to detect outlier or apply conventional outlier detection methods to the reduced data, which could deteriorate the performance of outlier detection as only considering part of the information from data. Few studies have been done to combine these two strategies to do outlier detection. In this paper, we proposed an outlier detection method based on Variational Autoencoder (VAE), which combines low-dimensional representation and reconstruction error to detect outliers. Specifically, we first model the data use VAE, then extract four outlier scores from VAE model, finally propose an ensemble method to combine the four outlier scores. The experiments conducted on six real-world datasets show that the proposed method performs better than or at least comparable to state of the art methods.