Isometric Gaussian Process Latent Variable Model for Dissimilarity Data release_yzwh6sdgynfa5g3n7jm5rhouke

by Martin Jørgensen, Søren Hauberg

Released as a article .

2021  

Abstract

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.
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Type  article
Stage   submitted
Date   2021-06-08
Version   v2
Language   en ?
arXiv  2006.11741v2
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