2002 Royal Statistical Society Conference, University of Plymouth, 2-6 September


Keynote paper in session on “Future perspectives in statistical image analysis”



Spatio-temporal analysis including multi-objective orthogonalisation and independent component analysis


Allan Aasbjerg Nielsen


Informatics and Mathematical Modelling

Technical University of Denmark

Richard Petersens Plads, Building 321

DK-2800 Kongens Lyngby, Denmark

aa@imm.dtu.dk, http://www.imm.dtu.dk/~aa/


The talk will deal with a number of methods for (orthogonal) transformation of multivariate data including



All these methods are useful in exploratory multivariate data analysis where we consider the observed data as indirect measurements of underlying, latent structures or factors that cannot be observed directly.  Some of the methods are specifically suited for data that vary spatially or temporally and some can be tailored to perform multi-objective orthogonalisation of for instance both spatial and temporal autocorrelation in spatio-temporal data.  Recently, independent component analysis (ICA) has emerged.  ICA can be seen as an interesting extension to principal component analysis (PCA) specifically suited for non-Gaussian latent factors.  Similarities and differences between more classical methods such as PCA and ICA will be described.  Application examples of the transformations will be given.  The data used in the examples include



Also, examples on application of some of the methods mentioned to change detection in bi-temporal, multivariate data will be given.  Finally, non-linear extensions to some of the methods will be described very briefly.