Linear and Nonlinear Multiset Canonical Correlation Analysis

Klaus Baggesen Hilger, Allan Aasbjerg Nielsen, Rasmus Larsen, Knut Conradsen

AbstractThis paper deals with decompositioning of multiset data. Friedman's alternating conditional expectations (ACE) algorithm is extended to handle multiple sets of variables of different mixtures. The new algorithm finds estimates of the optimal transformations of the involved variables that maximize the sum of the pair-wise correlations over all sets. The new algorithm is termed multi-set ACE (MACE) and can find multiple orthogonal eigensolutions. MACE is a generalization of the linear multiset correlations analysis (MCCA). It handles multivariate multisets of arbitrary mixtures of both continuous and categorical variables by applying only bivariate scatterplot smoothers for which the data analyst may specify appropriate restrictions when performing an exploratory analysis of the data.
TypeConference paper [Abstract]
ConferenceEleventh International Workshop on Matrices and Statistics, invited contribution
Year2002    Month August
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
SeriesTechnical University of Denmark
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics, Geoinformatics