Canonical Information Analysis

Jacob Schack Vestergaard, Allan Aasbjerg Nielsen

AbstractCanonical correlation analysis is an established multivariate statistical method in which correlation between linear combinations of multivariate sets of variables is maximized. In canonical information analysis introduced here, linear correlation as a measure of association between variables is replaced by the information theoretical, entropy based measure mutual information, which is a much more general measure of association. We make canonical information analysis feasible for large sample problems, including for example multispectral images, due to the use of a fast kernel density estimator for entropy estimation. Canonical information analysis is applied successfully to 1) simple simulated data to illustrate the basic idea and evaluate performance, 2) fusion of weather radar and optical geostationary satellite data in a situation with heavy precipitation, and 3) change detection in optical airborne data. The simulation study shows that canonical information analysis is as accurate as and much faster than algorithms presented in previous work, especially for large sample sizes.
KeywordsInformation theory, probability density function estimation, Parzen windows, entropy, mutual information maximization, canonical mutual information analysis, CIA, approximate entropy.
TypeJournal paper [With referee]
JournalISPRS Journal of Photogrammetry and Remote Sensing
Year2015    Month January    Vol. 101    pp. 1-9
ISBN / ISSN10.1016/j.isprsjprs.2014.11.002
NoteMatlab code at
Electronic version(s)[pdf]
Publication link
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics