Saturday, January 04, 2014

Nonlinear Compressive Sensing - request -

In Sunday Morning Insight: A Quick Panorama of Sensing from Direct Imaging to Machine Learning, we saw that the main difference between traditional sensing and machine learning mostly hinges on the first layer in NN parlance. In Compressive sensing, the first step is a linear one, a matrix-vector multiplication, while in Machine Learning the input space is traditionally projected nonlinearly on some other feature space.

Two years, following up on a query on LinkedIn, Matthieu Puigt, Phil Schniter and Karthikeyan Natesan Ramamurthy provided a small survey of what the next step is in compressive sensing: a nonlinear first stage. The resulting list of approaches was featured in Nonlinear Compressive Sensing

Since then, another effort was undertaken by Yonina Eldar, Allen Yang, Roy Dong and Henrik Ohlsson that aimed at listing nonlinear compressive sensing approaches in the NonlinearCS blog.

Here are some entries/papersI gathered that I had not mentioned before:

1 comment:

yhli said...

Nonlinear regression problems with sparse parameters might be viewed as nonlinear compressive sensing problems. Then there are some results from the statistics community.