Last week, I attended this conference in Berlin, and much like the last CSA conference, it was very nice. This year, most of the talks followed one of three themes:

- Application-driven compressed sensing
- Quadratic or bilinear problems
- Clustering in graphs or Euclidean space

Examples of application-driven CS include theoretical results for radar-inspired sensing matrices and model-based CS for quantitative MRI. Readers of this blog are probably familiar with the prototypical quadratic problem (phase retrieval), and bilinear problems include blind deconvolution and self-calibration. Recently, I have blogged quite a bit about clustering in Euclidean space (specifically, k-means clustering), but I haven’t written much about clustering in graphs (other than its application to phase retrieval). For the remainder of this entry, I will discuss two of the talks from CSA2015 that covered different aspects of graph clustering.

Continue reading Compressed Sensing and its Applications 2015