Citation:
P. Irofti, “The effect of atom replacement strategies on dictionary learning”, in INTERNATIONAL TRAVELING WORKSHOP ON INTERACTIONS BETWEEN SPARSE MODELS AND TECHNOLOGY, Aalborg, Denmark, 2016.
Date Published:
24 August
Abstract:
The sparse representations field presents a wide set of algorithms for learning overcomplete dictionaries. During the learning process many of the dictionary columns remain unused by the resulting representations. In this paper we present a few replacement strategies and their direct impact on a set of popular algorithms such as K-SVD. Experiments show significant reductions in the representation error and also evidentiate clear differences between the strategies.
