H-tSNE: Hierarchical Nonlinear Dimensionality Reduction

Dimensionality reduction (DR) is often integral when analyzing high-dimensional data across scientific, economic, and social networking applications. For data with a high order of complexity, nonlinear approaches are often needed to identify and represent the most important components. We propose a novel DR approach that can incorporate a known underlying hierarchy. Specifically, we extend the widely used t-Distributed Stochastic Neighbor Embedding technique (t-SNE) to include hierarchical information and demonstrate its use with known or unknown class labels. We term this approach "H-tSNE." Such a strategy can aid in discovering and understanding underlying patterns of a dataset that is heavily influenced by parent-child relationships. Without integrating information that is known a priori, we suggest that DR cannot function as effectively. In this regard, we argue for a DR approach that enables the user to incorporate known, relevant relationships even if their representation is weakly expressed in the dataset.