Dynamic ℓ1 Reconstruction
published: Aug. 26, 2013, recorded: July 2013, views: 4479
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
Sparse signal recovery often involves solving an ℓ1-regularized optimization problem. Most of the existing algorithms focus on the static settings, where the goal is to recover a fixed signal from a fixed system of equations. This talk will have two parts. In the first, we present a collection of homotopy-based algorithms that dynamically update the solution of the underlying ℓ1 problem as the system changes. The sparse Kalman filter solves an ℓ1-regularized Kalman filter equation for a time-varying signal that follows a linear dynamical system. Our proposed algorithm sequentially updates the solution as the new measurements are added and the old measurements are removed from the system.
In the second part of the talk, we will discuss a continuous time "algorithm" (i.e. a set of coupled nonlinear differential equations) for solving a class of sparsity regularized least-square problems. We characterize the convergence properties of this neural-net type system, with a special emphasis on the case when the final solution is indeed sparse.
This is joint work with M. Salman Asif, Aurele Balavoine, and Chris Rozell
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: