Distance Correlation Autoencoder

Rick Wang, Amir-Hossein Karimi, Ali Ghodsi

Abstract: This paper looks at applying a new objective function based on distance correlation for supervised dimensionality reduction using autoencoders. We elaborate on different properties of distance correlation to illustrate that maximizing it would be beneficial for supervised dimensionality reduction. We also described the general structure of the autoencoder used to maximize distance correlation. Lastly, our model was applied to a variety of problem sets from toy examples to regression and image datasets and obtained good results in general.