GTM: the generative topographic mapping
Neural Computation
A generative probabilistic approach to visualizing sets of symbolic sequences
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Computational intelligence in astronomy --- a win-win situation
TPNC'12 Proceedings of the First international conference on Theory and Practice of Natural Computing
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We present a probabilistic generative approach for constructing topographic maps of light curves from eclipsing binary stars. The model defines a low-dimensional manifold of local noise models induced by a smooth non-linear mapping from a low-dimensional latent space into the space of probabilistic models of the observed light curves. The local noise models are physical models that describe how such light curves are generated. Due to the principled probabilistic nature of the model, a cost function arises naturally and the model parameters are fitted via MAP estimation using the Expectation-Maximisation algorithm. Once the model has been trained, each light curve may be projected to the latent space as the the mean posterior probability over the local noise models. We demonstrate our approach on a dataset of artificially generated light curves and on a dataset comprised of light curves from real observations.