Data decomposition: from independent component analysis to sparse representations

  Department of Engineering Science, University of Oxford, Oxford, United Kingdom
DOI
10.7287/peerj.preprints.27456v1
Published
Accepted
Subject Areas
Artificial Intelligence, Computer Vision, Data Mining and Machine Learning, Data Science
Keywords
Data decomposition, Independent Component Analysis, Sparse Representations, Sparse Component Analysis, Blind Source Separation, Matrix Factorization, Vision, Feature Extraction
Copyright
© 2018 Roussos
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Roussos E. 2018. Data decomposition: from independent component analysis to sparse representations. PeerJ Preprints 6:e27456v1

Abstract

This paper provides a unifying review of some recent approaches of decomposing data, images, and signals into sets of components. We start from the classical algebraic method of singular value decomposition and then introduce principal and independent component analysis. The text continues with the main subject of this paper, sparse representation and decomposition, emphasizing their biological plausibility. In this paper emphasis will be given to the geometric perspective, with the mathematics kept to a minimum.

Author Comment

This is a preprint submission to PeerJ Preprints.