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Isometric Gaussian Process Latent Variable Model

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I will talk about an unsupervised model where the latent variable respects the distances and topology of the data. The ISO -GPLVM model controls the Riemannian geometry of the data manifold. This gives the latent space a stochastic distance measure. Manifold distances are modeled locally as Nakagami distributions. Stochastic distances aim to be close to the observed distances over a neighborhood graph. Global structure preserves by the use of censoring.

This talk is part of the ML@CL Seminar Series series.

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