Low-cost Unsupervised Outlier Detection by Autoencoders with Robust Estimation

Abstract

Recently, an unsupervised outlier detection method based on the reconstruction errors of an autoencoder (AE), which achieves high detection accuracy, was proposed. This method, however, requires a high calculation cost because of its ensemble scheme. Therefore, in this paper, we propose a novel AE-based unsupervised method that can achieve high detection performance at a low calculation cost. Our method introduces the concept of robust estimation to appropriately restrict reconstruction capability and ensure robustness. Experimental results on several public benchmark datasets show that our method outperforms well-known outlier detection methods and at a low calculation cost.