Sparse representation has been widely used in machine learning, signal processing and communications. K-SVD, which generalizes k-means clustering, is one of the most famous algorithms for sparse representation and dictionary learning. K-SVD is an iterative method that alternates between encoding the data sparsely by using the current dictionary and updating the dictionary based on the sparsely represented data. In this paper, we introduce a single-pass K-SVD method. In this method, the previous input data are first summarized as a condensed representation of weighted samples. Then, we developed a weighted K-SVD algorithm to learn a dictionary from the union of this representation and the newly input data. Experimental results show that our approach can approximate K-SVD's performance well by consuming considerably less storage resource.