  Feature extraction becomes increasingly important as data grows high
dimensional. Autoencoder as a neural network based feature extraction method
achieves great success in generating abstract features of high dimensional
data. However, it fails to consider the relationships of data samples which may
affect experimental results of using original and new features. In this paper,
we propose a Relation Autoencoder model considering both data features and
their relationships. We also extend it to work with other major autoencoder
models including Sparse Autoencoder, Denoising Autoencoder and Variational
Autoencoder. The proposed relational autoencoder models are evaluated on a set
of benchmark datasets and the experimental results show that considering data
relationships can generate more robust features which achieve lower
construction loss and then lower error rate in further classification compared
to the other variants of autoencoders.
