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Learning fast sparsifying overcomplete dictionaries

Rusu, Cristian and Thompson, John (2017) Learning fast sparsifying overcomplete dictionaries. In: 25th European Signal Processing Conference (EUSIPCO). IEEE, Kos, pp. 723-727. ISBN 9780992862671

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In this paper we propose a dictionary learning method that builds an over complete dictionary that is computationally efficient to manipulate, i.e., sparse approximation algorithms have sub-quadratic computationally complexity. To achieve this we consider two factors (both to be learned from data) in order to design the dictionary: an orthonormal component made up of a fixed number of fast fundamental orthonormal transforms and a sparse component that builds linear combinations of elements from the first, orthonormal component. We show how effective the proposed technique is to encode image data and compare against a previously proposed method from the literature. We expect the current work to contribute to the spread of sparsity and dictionary learning techniques to hardware scenarios where there are hard limits on the computational capabilities and energy consumption of the computer systems.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Staff Research and Publications
Depositing User: Caoimhe Ní Mhaicín
Date Deposited: 02 Jul 2018 10:16
Last Modified: 02 Jul 2018 10:16

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