Structure-Preserving Deep Autoencoder-based Dimensionality Reduction for Data Visualization

Here, we propose a structure-preserving deep autoencoder-based dimensionality reduction scheme for data visualization. For this, we introduce two regularizers for regularizing autoencoders. The proposed regularizers help the encoded feature space preserve the local and global structures present in the original feature space. A chosen reduced dimensionality of two or three for the encoded feature space enables us to visualize the extracted latent representations of the data using scatterplots. The proposed method has two variants, depending on which regularizer it uses. The proposed approach, moreover, is unsupervised and has predictability. We use three synthetic datasets and one real-world dataset to illustrate the effectiveness of the proposed method. We also visually compare it with three state-of-the-art data visualization schemes and discuss several future research directions.