Evaluation of K-SVD with different embedded sparse representation algorithms

The K-SVD algorithm is a powerful tool in finding an adaptive dictionary for a set of signals via using the sparse representation optimization and constrained singular value decomposition. In this paper, we first review the original K-SVD algorithm as well as some sparse representation algorithms including OMP, Lasso and recently proposed IITH. Secondly, we embed the Lasso and IITH sparse representation algorithms into the K-SVD process and establish two new different K-SVD algorithms. Finally, we have done extensive experiments to evaluate the performances of these derived K-SVD algorithms with different pursuit methods and these experiments show that the K-SVD with IITH has distinctive advantages in computational cost and signal recovery performance while the K-SVD with Lasso is not sensitive to initial conditions.