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Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
Martin Jørgensen · Søren Hauberg

Thu Jul 22 09:00 AM -- 11:00 AM (PDT) @
in Poster Session 5 »

We present a probabilistic model where the latent variable respects both the distances and the topology of the modeled data. The model leverages the Riemannian geometry of the generated manifold to endow the latent space with a well-defined stochastic distance measure, which is modeled locally as Nakagami distributions. These stochastic distances are sought to be as similar as possible to observed distances along a neighborhood graph through a censoring process. The model is inferred by variational inference based on observations of pairwise distances. We demonstrate how the new model can encode invariances in the learned manifolds.

Keywords: Gaussian Processes and Bayesian non-parametrics

Author Information

Martin Jørgensen (University of Oxford)
Søren Hauberg (Technical University of Denmark)

I was born, and now I exist.

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