Adaptive Compressive Sampling

Tuesday, December 8, 2015 - 1:25pm - 2:25pm
Jarvis Haupt (University of Minnesota, Twin Cities)
Fueled by our increasingly information-based and data-driven society, there is a growing need for computational methods capable of “making sense” of volumes of data that may be very high-dimensional, corrupted, or even largely incomplete. A unifying theme in many modern investigations into problems of this form is to exploit intrinsic low-dimensional structure to facilitate inference.
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