Imperial College London > Talks@ee.imperial > Featured talks > Sparse Signal Processing: A Geometric Approach

Sparse Signal Processing: A Geometric Approach

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If you have a question about this talk, please contact Professor Peter Cheung.

Sparse signal processing is a technique for acquiring and analyzing sparse signals efficiently. Many signals, including biomedical signals, sensor network measurements, and Netflix user preference data are sparse in the sense that they can be well-approximated by a small number of significant components. Signal sparsity allows for large saving in data collection or succinct interpretation of enormous amount of data.

In this talk we cover three aspects in sparse signal processing: compressive sensing, low-rank matrix completion and low-rank tensor completion. The sparse signals under consideration are represented as vectors, matrices and tensors (i.e., high-dimensional arrays), respectively. We show how different methods are required to treat different types of sparse signals, and how these techniques are linked by geometry. It turns out that linear subspaces not only provide insights into sparse signal processing theory but also help in developing practical algorithms.

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