Clustering before training large datasets — Case study: K-SVD

Training and using overcomplete dictionaries has been the subject of many developments in the area of signal processing and sparse representations. The main idea is to train a dictionary that is able to achieve good sparse representations of the items contained in a given dataset. The most popular approach is the K-SVD algorithm and in this paper we study its application to large datasets. The main interest is to speedup the training procedure while keeping the representation errors close to some specific values. This goal is reached by using a clustering procedure, called here T-mindot, which reduces the size of the dataset but keeps the most representative data items and a measure of their importance. Experimental simulations compare the running times and representation errors of the training method with and without the clustering procedure and they clearly show how effective T-mindot is.