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This research extends the concept of proportional analogies from Euclidean vector spaces to Riemannian manifolds, enabling analogy-based reasoning on non-Euclidean structures like spheres and probability distributions. By generalizing the parallelogram rule for these curved domains, the work addresses a critical gap in applying geometric deep learning techniques beyond flat space constraints. This advancement is significant for machine learning models operating in complex data landscapes where standard linear assumptions fail.
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A Generalized Parallelogram Rule for Proportional Analogies on Riemannian Manifolds

arXiv2 min read

arXiv:2608.14220v1 Announce Type: new Abstract: Analogies are quaternary relations of the form "a is to b as c is to d", usually denoted a : b :: c : d. This notion is formalized in particular with the notion of proportional analogy, which imposes some constraints on the valid analogies. Whereas proportional analogies have been studied mostly in symbolic domains and in vector spaces, their use is limited in non-Euclidean spaces. In this paper, we introduce a proportional analogy relation in Riemannian domains, extending the parallelogram rule used for arithmetic analogies in Euclidean spaces. We illustrate the introduced analogy on various manifolds, such as the sphere, shape spaces and manifolds of probability distributions.

Abstract:Analogies are quaternary relations of the form "a is to b as c is to d", usually denoted a : b :: c : d. This notion is formalized in particular with the notion of proportional analogy, which imposes some constraints on the valid analogies. Whereas proportional analogies have been studied mostly in symbolic domains and in vector spaces, their use is limited in non-Euclidean spaces. In this paper, we introduce a proportional analogy relation in Riemannian domains, extending the parallelogram rule used for arithmetic analogies in Euclidean spaces. We illustrate the introduced analogy on various manifolds, such as the sphere, shape spaces and manifolds of probability distributions.

Artificial Intelligence (cs.AI)

arXiv:2608.14220 [cs.AI]

(or arXiv:2608.14220v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.14220 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 202 words

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