Linear and Nonlinear Multiset Canonical Correlation Analysis (invited talk)

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    Abstract

    This 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.
    Original languageEnglish
    Title of host publicationEleventh International Workshop on Matrices and Statistics
    PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
    Publication date2002
    Publication statusPublished - 2002

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