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AI Seminar

A Geometric Perspective on Machine Learning

Partha Niyogi


 
Thursday, April 07, 2005
12:00pm - 1:30pm
175 ATL

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About the Event

Natural data (speech signals, images, etc.) live in very high dimensional spaces. However, there has always been the strong intuition that there are only a few explanatory degrees of freedom. One way to formalize this intuition is to model the data as lying on or near a low dimensional manifold embedded in the original high dimensional space. This point of view gives rise to a new class of geometrically motivated learning algorithms known as manifold learning algorithms. I will discuss a framework for manifold learning and show how the classical problems of statistical machine learning such as dimensionality reduction, clustering, classification, and regression may be treated within this framework. We will see how various geometric and topological invariants of an underlying unknown manifold may be estimated from random samples, and how ideas from geometry, statistics, and computer science may come together in a new and interesting way in this setting.

Additional Information

Contact: Dragomir Radev

Email: radev@umich.edu

Sponsor(s): AI Laboratory

Open to: Public