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Sparsity – The Beauty of Less is More
Abstract
With advances in sensor technology, data has become ubiquitous. To make sense of the data we have to solve higher and higher dimensional problems that may seem intractable. However, many high-dimensional problems have solutions that live in low-dimensional space. Sparsity is a way to exploit the low-dimensional structure of solutions to obtain feasible solutions for high-dimensional problems. In this talk, Professor Chan will introduce regularisation methods that enforce sparsity in solutions and their application to several image reconstruction problems, including single-molecule localisation microscopy and ground-based astronomy.



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