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A general framework for a principled hierarchical visualization of multivariate data

Kaban, A; Tino, P; Girolami, M; (2002) A general framework for a principled hierarchical visualization of multivariate data. In: Yin, H and Allinson, N and Freeman, R and Keane, J and Hubbard, S, (eds.) INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2002. (pp. 518 - 523). SPRINGER-VERLAG BERLIN

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Abstract

We present a general framework for interactive visualization and analysis of multi-dimensional data points. The proposed model is a hierarchical extension of the latent trait family of models developed in [4] as a generalization of GTM to noise models from the exponential family of distributions. As some members of the exponential family of distributions are suitable for modeling discrete observations, we give a brief example of using our methodology in interactive visualization and semantic discovery in a corpus of text-based documents. We also derive formulas for computing local magnification factors of latent trait projection manifolds.

Type:Proceedings paper
Title:A general framework for a principled hierarchical visualization of multivariate data
Event:3rd International Conference on Intelligent Data Engineering and Automated Learning
Location:MANCHESTER, ENGLAND
Dates:2002-08-12 - 2002-08-14
ISBN:3-540-44025-9
Keywords:MODEL
UCL classification:UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science

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