Department of Mathematics,
University of California San Diego
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Math 288 - Probability & Statistics
Antonio Auffinger
Northwestern University (tuca@northwestern.edu)
Dimension Reduction Methods for Data Visualization
Abstract:
The purpose of dimension reduction methods for data visualization is to project high dimensional data to 2 or 3 dimensions so that humans can understand some of its structure. In this talk, we will give an overview of some of the most popular and powerful methods in this active area. We will then focus on two algorithms: Stochastic Neighbor Embedding (SNE) and Uniform Manifold Approximation and Projection (UMAP). Here, we will present new rigorous results that establish an equilibrium distribution for these methods when the number of data points diverge in the presence of pure noise or with a planted signal.
Tianyi Zheng
October 15, 2024
3:00 PM
AP&M 6402
Research Areas
Probability Theory Statistics****************************
