Nonlinear Dimensionality Reduction
Description
Working with lower dimensional representations of data can be valuable for simplifying models, removing noise, and visualization. When data is distributed in geometrically complicated ways, tools such as PCA can quickly run into limitations due to their linear nature. In this tutorial, we will dive into dimension reduction for when data is distributed in ways that have nontrivial topology and curvature. We’ll build up our understanding of these approaches alongside classical ideas from topology and differential geometry and consider their interplay. Finally, we will explore some relationships between nonlinear dimension reduction and stochastic dynamics.
Zoom meeting: https://mit.zoom.us/j/94930902794?pwd=M0diMUgvVEVZS2U2NFc4Z2c2dDZQQT09
Password: 682860
Speaker Bio
Christian Bueno is a mathematics PhD student at the University of California, Santa Barbara advised by Paul J. Atzberger. His research broadly focuses on theoretical properties of different machine learning methods for non-Euclidean problem domains. Previously, Christian has been a three-time intern at NASA Glenn Research Center and continues to collaborate on various projects.
Additional Info
We are looking for speakers to present at the series in the Fall! If you would like to host a tutorial, please contact Jenelle Feather (jfeather@mit.edu) or Nhat Le (nmle@mit.edu)