The SMALLbox, a framework for sparse representation and dictionary learning.
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 explorationof new provably good methods to obtain inherently data-driven sparse models, which are able to cope with large-scale and complicated data.
The main focus of research in the area of sparse representations is in developing reliable algorithms with provable performance and bounded complexity. Yet, such approaches are simply inapplicable in many scenarios for which no suitable sparse model is known. Moreover, the success of sparse models heavily depends on the choice of a “dictionary” to reflect the natural structures of a class of data. Inferring a dictionary from training data is a key to the extension of sparse models for new exotic types of data.
The SMALLbox 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-theart toolboxes. As an open source MATLAB toolbox, the SMALLbox can be seen as a tool for reproducible research in the sparse representations research community.
You will find below the SMALLbox and its documentation.
SMALLbox v1.51 : contains the Audio Inpainting toolbox, cvx and ompGabor.
SMALLbox v1.9 : contains MAjorization Minimization, MOD, MAP DL, (Algebraic Pursuit) ALPS, declipping problem.
SMALLbox v2.0 (March 2012) : compatibilty issues fixed, bugs corrected, ease of the use of add-ons.
SMALLbox v2.1 (October 2012) : fixing several bugs - the most important ones regarding compatibility with more recent versions of MATLAB..