Generalized sampling and infinite-dimensional compressed sensing

Add to your list(s) Download to your calendar using vCal

• Anders Hansen (University of Cambridge)
• Thursday 24 March 2011, 16:00-17:00
• Seminar Room 1, Isaac Newton Institute for Mathematical Sciences.

If you have a question about this talk, please contact ai10.

will discuss a generalization of the Shannon Sampling Theorem that allows for reconstruction of signals in arbitrary bases. Not only can one reconstruct in arbitrary bases, but this can also be done in a completely stable way. When extra information is available, such as sparsity or compressibility of the signal in a particular bases, one may reduce the number of samples dramatically. This is done via Compressed Sensing techniques, however, the usual finite-dimensional framework is not sufficient. To overcome this obstacle I’ll introduce the concept of Infinite-Dimensional Compressed Sensing.

This talk is part of the LMS Invited Lectures 2011 series.

This talk is included in these lists:

• All CMS events
• All Talks (aka the CURE list)
• CMS Events
• Cosmology, Astrophysics and General Relativity
• DPMMS Lists
• DPMMS info aggregator
• DPMMS lists
• Interested Talks
• LMS Invited Lectures 2011
• School of Physical Sciences
• Seminar Room 1, Isaac Newton Institute for Mathematical Sciences
• Statistical Laboratory Graduate Seminars
• Statistical Laboratory info aggregator
• Statistics Group
• Statistics Reading Group
• bld31

Note that ex-directory lists are not shown.