An Efficient Dictionary Learning Algorithm for Sparse Representation

Sparse and redundant representation of data assumes an ability to describe signals as linear combinations of a few atoms from a dictionary. If the model of the signal is unknown, the dictionary can be learned from a set of training signals. Like the K-SVD, many of the practical dictionary learning algorithms are composed of two main parts: sparse-coding and dictionary-update. This paper first proposes a Stagewise least angle regression (St-LARS) method for performing the sparse-coding operation. The St-LARS applies a hard-thresholding strategy into the original least angle regression (LARS) algorithm, which enables it to select many atoms at each iteration and thus results in fast solutions while still provides good results. Then, a dictionary update method named approximated singular value decomposition (ASVD) is used on the dictionary update stage. It is a quick approximation of the exact SVD computation and can reduce the complexity of it. Experiments on both synthetic data and 3-D image denoising demonstrate the advantages of the proposed algorithm over other dictionary learning methods not only in terms of better trained dictionary but also in terms of computation time.