EECS 500 Seminar

Subspaces and Machine Learning

Laura Balzano

Assistant Professor
University of Michigan - Department of EECS
Friday, October 11, 2013
12:30pm - 1:30pm
1311 EECS

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About the Event

Low-dimensional linear subspace approximations to high-dimensional data have provided a powerful tool to many areas of engineering and science: problems of estimation, detection and prediction, with applications such as network monitoring, collaborative filtering, object tracking in computer vision, and environmental sensing. In this talk I will give you a reminder of what are subspaces, the SVD, and PCA. I'll discuss several of the applications that use these for modeling and approximation. Then I'll introduce a little bit of optimization, and talk about why optimization theory and algorithms offer a very nice framework to pose and solve new engineering problems related to subspace identification.

Additional Information

Contact: Ann Pace

Phone: 763-5022


Sponsor(s): University of Michigan, Department of Electrical Engineering & Computer Science

Open to: Public