Canonical analysis based on mutual information

Allan Aasbjerg Nielsen, Jacob Schack Vestergaard

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Canonical correlation analysis (CCA) is an established multi-variate statistical method for finding similarities between linear combinations of (normally two) sets of multivariate observations. In this contribution we replace (linear) correlation as the measure of association between the linear combinations with the information theoretical measure mutual information (MI). We term this type of analysis canonical information analysis (CIA). MI allows for the actual joint distribution of the variables involved and not just second order statistics. While CCA is ideal for Gaussian data, CIA facilitates analysis of variables with different genesis and therefore different statistical distributions and different modalities. As a proof of concept we give a toy example. We also give an example with one (weather radar based) variable in the one set and eight spectral bands of optical satellite data in the other set.
Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2015)
PublisherIEEE
Publication date2015
Pages1068-1071
ISBN (Print)978-1-4799-7929-5
DOIs
Publication statusPublished - 2015
EventThe International Geoscience and Remote Sensing Symposium: Remote Sensing: Understanding the Earth for a Safer World - MiCo — Milano Congressi, Milan, Italy
Duration: 26 Jul 201531 Jul 2015
http://www.igarss2015.org/

Conference

ConferenceThe International Geoscience and Remote Sensing Symposium
LocationMiCo — Milano Congressi
Country/TerritoryItaly
CityMilan
Period26/07/201531/07/2015
Internet address

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