SMALLbox - an evaluation framework for sparse representations and dictionary learning algorithms

  • Authors:

  • Ivan Damnjanovic;Matthew E. P. Davies;Mark D. Plumbley

  • Affiliations:

  • Queen Mary University of London, Centre for Digital Music, London, United Kingdom;Queen Mary University of London, Centre for Digital Music, London, United Kingdom;Queen Mary University of London, Centre for Digital Music, London, United Kingdom

  • Venue:
  • LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
  • Year:
  • 2010

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Abstract

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.