Sparse and Redundant Representations: Theory and Applications

In this talk we describe some of the recent advances in sparse and redundant representations of signals. Specific applications are described ranging from the matrix completion problem, to video retrieval and compressive sensing. Such problems are formulated and solved using deterministic and stochastic approaches. Specific examples are shown from image and video processing and comparisons are made with the state-of-the-art algorithms. Open problems and future research directions are discussed.