Tree Preserving Embedding

Abstract

Visualization techniques for complex data are a workhorse of modern scientific pursuits. The goal of visualization is to embed high dimensional data in a low dimensional space, while preserving structure in the data relevant to exploratory data analysis, such as the existence of clusters. However, existing visualization methods often either entirely fail to preserve clusters in embeddings due to the crowding problem or can only preserve clusters at a single resolution. Here, we develop a new approach to visualization, tree preserving embedding (TPE). Our approach takes advantage of the topological notion of connectedness to provably preserve clusters at all resolutions. Our performance guarantee holds for finite samples, which makes TPE a robust method for applications. Our approach suggests new strategies for robust data visualization in practice.