Feature extraction of linear separability using robust autoencoder with distance metric

Abstract

This paper proposes a robust autoencoder with Wasserstein distance metric to extract the linear separability features from the input data. To minimize the difference between the reconstructed feature space and the original feature space, using Wasserstein distance realizes a homeomorphic transformation of the original feature space, i.e., the so-called the reconstruction of feature space. The autoencoder is used for features extraction of linear separability in the reconstructed feature space. Experiment results on real datasets show that the proposed method reaches up 0.9777 and 0.7112 on the low-dimensional and high-dimensional datasets in extracted accuracies, respectively, and also outperforms competitors. Results also confirm that compared with feature metric-based methods and deep network architectures-based method, the linear separabilities of those features extracted by distance metric-based methods win over them. More importantly, the linear separabilities of those features obtained by evaluating distance similarity of the data are better than those obtained by evaluating feature importance of data. We also demonstrate that the data distribution in the feature space reconstructed by a homeomorphic transformation can be closer to the original data distribution.