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ICA
2010
Springer

SMALLbox - An Evaluation Framework for Sparse Representations and Dictionary Learning Algorithms

9 years 2 days ago
SMALLbox - An Evaluation Framework for Sparse Representations and Dictionary Learning Algorithms
SMALLbox is a new foundational framework for processing signals, using adaptive sparse structured representations. The main aim of SMALLbox is to become a test ground for exploration of new provably good methods to obtain inherently data-driven sparse models, able to cope with large-scale and complicated data. The toolbox provides an easy way to evaluate these methods against state-of-the art alternatives in a variety of standard signal processing problems. This is achieved trough a unifying interface that enables a seamless connection between the three types of modules: problems, dictionary learning algorithms and sparse solvers. In addition, it provides interoperability between existing state-of-the-art toolboxes. As an open source MATLAB toolbox, it can be also seen as a tool for reproducible research in the sparse representations research community.
Ivan Damnjanovic, Matthew E. P. Davies, Mark D. Pl
Added 07 Dec 2010
Updated 07 Dec 2010
Type Conference
Year 2010
Where ICA
Authors Ivan Damnjanovic, Matthew E. P. Davies, Mark D. Plumbley
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