The Latent Variable Data Model for Exploratory DataAnalysis and Visualisation: A Generalisation of theNonlinear Infomax Algorithm

Authors:
Mark Girolami
Affiliations:
Department of Computing and Information Systems, University of Paisley, High Street, Paisley, Scotland, PA1 2BE E-mail: may4@tutor.open.ac.uk
Venue:
Neural Processing Letters
Year:
1998

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Abstract

This paper presents a generalisation of the nonlinear’Infomax‘ algorithm based on the linear latentvariable model of factor analysis. The algorithm isbased on an information theoretic index for projectionpursuit which defines linear projections of observeddata onto subspaces of lower dimension. This isapplied to the visualisation and interpretation ofcomplex high dimensional data and is empiricallycompared with the recently developed GenerativeTopographic Mapping.