Feature variance regularization method for autoencoder-based one-class classification

Abstract

One-class classification (OCC) has been being used in various research fields, since it is able to design classifiers using the data from a single class. Among various methods for OCC, principal component analysis (PCA) is one of the most widely used ones, whose nonlinear extension can be performed by autoencoders. Although the existing regularization methods, such as L1 and L2 regularizations, can prevent the total variance of autoencoder's bottleneck layer from exploding, they cannot effectively reduce it below certain levels to alleviate the problem of variance inflation. To this end, in this work, a novel variance regularization method is proposed, which directly controls the total variance of the bottleneck layer. Case studies are carried out using two datasets (MNIST dataset and Tennessee Eastman dataset) to illustrate its ability as a regularizer, and as an enhancer for the design of one-class classifiers (in terms of performance and training time).