Latent Variables, Topographic Mappings and Data Visualization

Christopher Bishop

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Most pattern recognition tasks, such as regression, classification and novelty detection, can be viewed in terms of probability density estimation. A powerful approach to probabilistic modelling is to represent the observed variables in terms of a number of hidden, or latent, variables. One well-known example of a hidden variable model is the mixture distribution in which the hidden variable is the discrete component label. In the case of continuous latent variables we obtain models such as factor analysis. In this paper we provide an overview of latent variable models, and we show how a particular form of linear latent variable model can be used to provide a probabilistic formulation of the well-known technique of principal components analysis (PCA). By extending this technique to mixtures, and hierarchical mixtures, of probabilistic PCA models we are led to a powerful interactive algorithm for data visualization. We also show how the probabilistic PCA approach can be generalized to non-linear latent variable models leading to the Generative Topographic Mapping algorithm (GTM). Finally, we show how GTM can itself be extended to model temporal data.
Original languageEnglish
Title of host publicationNeural Nets WIRN VIETRI-97
Subtitle of host publicationProceedings of the 9th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 22–24 May 1997
EditorsMaria Marinaro, Roberto Tagliaferri
PublisherSpringer London
Number of pages30
ISBN (Electronic)978-1-4471-1520-5
ISBN (Print)978-1-4471-1522-9
Publication statusPublished - 1998

Publication series

NamePerspectives in Neural Computing
PublisherSpringer London
ISSN (Print)1431-6854


Dive into the research topics of 'Latent Variables, Topographic Mappings and Data Visualization'. Together they form a unique fingerprint.

Cite this