Regularized K-SVD

The problem of dictionary learning (DL) for sparse representations can be approximately solved by several algorithms. Regularization of the optimization objective (representation error) was proved useful, since it avoids possible bottlenecks due to nearly linearly dependent atoms. We show here how the well-known K-SVD algorithm can be adapted to the regularized DL problem, despite previous claims that such an adaptation seems impossible. We also provide numerical evidence that regularized K-SVD is better than Simultaneous Codeword Optimization, the most prominent algorithm dedicated to the regularized DL problem.