Description:

t-SNE and Local 1D Structures

Anna Ma, Assistant Professor, UC Irvine, Department of Mathematics

Abstract: Data visualization is a vital task in data exploration, especially in the presence of large-scale data sets. Rudimentary approaches for data visualization, such as scatter plots, histograms, and pie charts, can only represent a small number (typically, 1-2) of features at a time. Furthermore, such methods often lack the sophistication to capture higher dimensional structures in their representations. Fortunately, new approaches to high-dimensional data visualization, such as the t-distributed stochastic neighbor embedding (t-SNE) algorithm, have been proposed in recent years. One of t-SNE’s more interesting properties is its tendency to preserve local linear data structures while successfully representing clusterable data. Despite its wide success, there is limited mathematical understanding of the algorithm. In this talk, we will discuss the t-SNE algorithm and present theoretical guarantees for t-SNE’s output to answer the question: does t-SNE preserve 1-dimensional curves? The work presented is joint with Kat Dover and Roman Vershynin.