Probabilistic Visualisation of High-Dimensional Binary Data

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Michael Tipping


We present a probabilistic latent-variable framework for data visu(cid:173) alisation, a key feature of which is its applicability to binary and categorical data types for which few established methods exist. A variational approximation to the likelihood is exploited to derive a fast algorithm for determining the model parameters. Illustrations of application to real and synthetic binary data sets are given.