Autoencoder-based outlier detection for sparse, high dimensional data
Wanghu Chen, Huijun Li, Jing Li, Arshad Ali · 2020
Outlier detection is essential in many data mining tasks. For high-dimensional data, its outlier detection often faces two challenges caused by sparse spatial distribution of data and big difficulties to get enough class labels. Therefore, it is valuable to explore a simpler and more effective approach to unsupervised outlier detection. In this paper, focusing on high-dimensional sparse data, an unsupervised outlier detection approach based on autoencoders and Robust PCA is proposed. Because Robust PAC has greater advantages in feature extraction of high-dimensional data and autoencoder has powerful capabilities in the reconstruction of normal data, the proposed approach can effectively address the two problems concerned above. The proposed approach is compared with some representative approaches, including ABOD, KNN, LOF, SOS and SOD, on eight well-known public datasets. The experiments show that compared with them, the proposed approach has advantages in both precision and recall rate, and can more accurately distinguish between normal data and outliers.