1 research outputs found
Fast Algorithms for Delta-Separated Sparsity Projection
We describe a fast approximation algorithm for the -separated
sparsity projection problem. The -separated sparsity model was
introduced by Hegde, Duarte and Cevher (2009) to capture the firing process of
a single Poisson neuron with absolute refractoriness. The running time of our
projection algorithm is linear for an arbitrary (but fixed) precision and it is
both a head and a tail approximation. This solves a problem of Hegde, Indyk and
Schmidt (2015). We also describe how our algorithm fits into the approximate
model iterative hard tresholding framework of Hegde, Indyk and Schmidt (2014)
that allows to recover -separated sparse signals from noisy random
linear measurements. The resulting recovery algorithm is substantially faster
than the existing one, at least for large data sets.Comment: 19 page