Recursive least squares dictionary learning algorithm

We present the Recursive Least Squares Dictionary Learning Algorithm, RLSDLA, which can be used for learning overcomplete dictionaries for sparse signal representation. Most Dictionary Learning Algorithms presented earlier, for example ILS-DLA and K-SVD, update the dictionary after a batch of training vectors has been processed, usually using the whole set of training vectors as one batch. The training set is used iteratively to gradually improve the dictionary. The approach in RLS-DLA is a continuous update of the dictionary as each training vector is being processed. The core of the algorithm is compact and can be effectively implemented. The algorithm is derived very much along the same path as the recursive least squares (RLS) algorithm for adaptive filtering. Thus, as in RLS, a forgetting factor can be introduced and easily implemented in the algorithm. Adjusting in an appropriate way makes the algorithm less dependent on the initial dictionary and it improves both convergence p...

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Karl Skretting, Kjersti Engan
• Keywords: Algorithm, Artificial Intelligence, Dictionary Learning Algorithms, Training Vector, TSP 2010