Astronomy and Astrophysics – Astrophysics
Scientific paper
2006-09-05
Astronomy and Astrophysics
Astrophysics
to appear in Lecture notes in Artificial Intelligence vol. 4265, the (refereed) proceedings of the Ninth International confere
Scientific paper
10.1007/11893318_15
Parametric Embedding (PE) has recently been proposed as a general-purpose algorithm for class visualisation. It takes class posteriors produced by a mixture-based clustering algorithm and projects them in 2D for visualisation. However, although this fully modularised combination of objectives (clustering and projection) is attractive for its conceptual simplicity, in the case of high dimensional data, we show that a more optimal combination of these objectives can be achieved by integrating them both into a consistent probabilistic model. In this way, the projection step will fulfil a role of regularisation, guarding against the curse of dimensionality. As a result, the tradeoff between clustering and visualisation turns out to enhance the predictive abilities of the overall model. We present results on both synthetic data and two real-world high-dimensional data sets: observed spectra of early-type galaxies and gene expression arrays.
Kaban Ata
Nolan Louisa
Raychaudhury Somak
Sun Jianyong
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