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arXiv · 1908.07377

Expected path length on random manifolds

David Eklund·Søren Hauberg
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Abstract

Manifold learning seeks a low dimensional representation that faithfully captures the essence of data. Current methods can successfully learn such representations, but do not provide a meaningful set of operations that are associated with the representation. Working towards operational representation learning, we endow the latent space of a large class of generative models with a random Riemannian metric, which provides us with elementary operators. As computational tools are unavailable for random Riemannian manifolds, we study deterministic approximations and derive tight error bounds on expected distances.

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David Eklund, Søren Hauberg. 2019-08-20. Expected path length on random manifolds. https://arxiv.org/abs/1908.07377

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