Recursive Compressive Sampling
We propose a method for performing compressed sensing (CS) on streaming data. The method comprises sequentially processing the input stream through overlapping windowing via:
a) recursive sampling, in which previously observed portion of the stream is leveraged in obtaining new samples, and
b) recursive estimation, in which previously obtained estimates are used for warm-start in iterative LASSO solvers.
We study the computational complexity of the proposed scheme as a function of the window size as well as the sliding step. Furthermore, we highlight the benefit of sampling redundant information in reducing the estimation error variance, and propose using voting strategies for further ameliorating the robustness of the estimation procedure.
Our simulation studies illustrate substantive speedup and improved estimation accuracy over a "naive approach."
(This is joint work with O. Ocal)
Keywords: Compressed Sensing, recursive estimation, iterative optimization, LASSO
Posted by Runwei Zhang on Thursday 23 January 2014 at 13:14