Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
release_yzwh6sdgynfa5g3n7jm5rhouke
by
Martin Jørgensen, Søren Hauberg
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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2006.11741v2
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