GTM: the generative topographic mapping
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Visual Data Mining: Techniques and Tools for Data Visualization and Mining
Visual Data Mining: Techniques and Tools for Data Visualization and Mining
The Latent Process Decomposition of cDNA Microarray Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Probabilistic latent semantic visualization: topic model for visualizing documents
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.