Sparse Representations from Inverse Problems to Pattern Recognition
published: July 30, 2009, recorded: June 2009, views: 14209
Report a problem or upload filesIf 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.
Sparse representations are at the core of many low-level signal processing procedures and are used by most pattern recognition algorithms to reduce the dimension of the search space. Structuring sparse representations fro pattern recognition applications requires taking into account invariants relatively to physical deformations such as rotation scaling or illumination. Sparsity, invariants and stability are conflicting requirements which is a source of open problems. Structured sparse representations with locally linear vector spaces are introduced for super-resolution inverse problems and pattern recognition.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !