A decomposition method for large-scale sparse coding in representation learning

In representation learning, sparse representation is a parsimonious principle that a sample can be approximated by a sparse superposition of dictionary atoms. Sparse coding is the core of this technique. Since the dictionary is often redundant, the dictionary size can be very large. Many optimization methods have been proposed in the literature for sparse coding. However, the efficiency of the optimization for a tremendous number of dictionary atoms is still a bottleneck. In this paper, we propose to use decomposition method for large-scale sparse coding models. Our experimental results show that our method is very efficient.