Abstract
Dictionary learning is an unsupervised learning task that finds a set of template vectors that expresses input signals by sparse linear combinations. There are currently several methods for dictionary learning, for example K-SVD and MOD. In this paper, a new dictionary learning method, namely K-normalized bilateral projections (K-NBP), is proposed, which uses faster low rank approximation. Experiments showed that the method was fast and when the number of iterations was limited, it outperforms K-SVD. This indicated that the method was particularly suited to large data sets with high dimension, where each iteration takes a long time. K-NBP was applied to an image reconstruction task where images corrupted by noise were recovered using a dictionary learned from other images.
