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The Latent Variable Data Model for Exploratory Data Analysis and Visualisation: A Generalisation of the Nonlinear Infomax Algorithm

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Abstract

This paper presents a generalisation of the nonlinear 'Infomax' algorithm based on the linear latent variable model of factor analysis. The algorithm is based on an information theoretic index for projection pursuit which defines linear projections of observed data onto subspaces of lower dimension. This is applied to the visualisation and interpretation of complex high dimensional data and is empirically compared with the recently developed Generative Topographic Mapping.

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  1. Department of Computing and Information Systems, University of Paisley, High Street, Paisley, Scotland, PA1 2BE E-mail

    Mark Girolami

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  1. Mark Girolami
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Girolami, M. The Latent Variable Data Model for Exploratory Data Analysis and Visualisation: A Generalisation of the Nonlinear Infomax Algorithm. Neural Processing Letters 8, 27–39 (1998). https://doi.org/10.1023/A:1009613012282

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