Evaluation of K-SVD with different embedded sparse representation algorithms
MetadataShow full item record
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.
Showing items related by title, author, creator and subject.
Li, Billy; Liu, Wan-Quan; An, Senjian; Krishna, Aneesh (2014)In this paper, we consider the problem of robust face recognition using color information. In this context, sparse representation-based algorithms are the state-of-the-art solutions for gray facial images. We will integrate ...
Zhang, X.; Pham, DucSon; Venkatesh, S.; Liu, Wan-Quan; Phung, D. (2015)Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation ...
Evaluation of K-SVD embedded with modified l<inf>1</inf>-norm sparse representation algorithmWang, M.; Liu, J.; Ma, S.; Liu, Wan-Quan (2017)© 2017, Springer Nature Singapore Pte Ltd. The K-SVD algorithm aims to find an adaptive dictionary for a set of signals by using the sparse representation optimization and constrained singular value decomposition. In this ...