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).
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  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.
|Title:||A general framework for a principled hierarchical visualization of multivariate data|
|Event:||3rd International Conference on Intelligent Data Engineering and Automated Learning|
|Dates:||2002-08-12 - 2002-08-14|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
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