An improved focuss-based learning algorithm for solving sparse linear inverse problems

We develop an improved algorithm for solving blind sparse linear inverse problems where both the dictionary (possibly overcomplete) and the sources are unknown. The algorithm is derived in the Bayesian framework by the maximum a posteriori method, with the choice of prior dis-tribution restricted to the class of concave/Schur-concave functions, which has been shown previously to be a suf-ficient condition for sparse solutions. This formulation leads to a constrained and regularized minimization prob-lem which can be solved in part using the FOCUSS (Fo-cal Underdetermined System Solver) algorithm for vector selection. We introduce three key improvements in the al-gorithm: an efficient way of adjusting the regularization parameter, column normalization that restricts the learned dictionary, and reinitialization to escape from local op-tima. Experiments were performed using synthetic data with matrix sizes up to 64x128, and the algorithm is shown to solve the blind identification problem, recovering both the dictionary and the sparse sources. The improved algo-rithm is shown to be much more accurate than the original FOCUSS-Dictionary Learning algorithm when using large matrices. We also test our algorithm on natural images, and show that a learned overcomplete representation can encode the data more efficiently than a complete basis at the same level of accuracy.