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Erschienen in:
2010 | OriginalPaper | Buchkapitel
SMALLbox - An Evaluation Framework for Sparse Representations and Dictionary Learning Algorithms
verfasst von : Ivan Damnjanovic, Matthew E. P. Davies, Mark D. Plumbley
Erschienen in: Latent Variable Analysis and Signal Separation
Verlag: Springer Berlin Heidelberg
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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.