Within-Class Scatter Constraint Based Randomized Autoencoder for One-Class Classification
Tianlei Wang, Jiuwen Cao, Xiaoping Lai, Q. M. Jonathan Wu · 2019
One-class classification (OCC) has attracted a lot of attentions for its strong applicability of outlier/anomaly detection in real-world applications. Among them, the mixed model based on the autoencoder (AE) is the commonly used OCC architecture. The recent random neural network based extreme learning machine (ELM) AE (ELM-AE) has been extended to OCC. But no constraints have been applied to the encoded feature in ELM-AE and the random parameters used in ELM-AE may result in meaningless features. In this paper, we propose a novel within-class scatter constraint based randomized AE (WSC-RAE) by introducing a constraint on the distribution of the encoded feature to restrict the solution space and thus avoid the meaningless features. The proposed WSC-RAE is then embedded into a multilayer one-class ELM (ML-OCELM) framework for OCC. Experiments on 17 benchmark datasets are carried out to demonstrate the superiority of WSC-RAE.