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.

• Type: Conference
• Year: 2010
• Where: ICA
• Authors: Ivan Damnjanovic, Matthew E. P. Davies, Mark D. Plumbley
• Keywords: Adaptive Sparse, Data-driven Sparse Models, ICA 2010, Signal Processing, Sparse