Riemannian Statistics for Any Type of Data

Fuente: arXiv
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Main Author: Rojas, Oldemar Rodriguez
Format: Preprint
Published: 2024
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author Rojas, Oldemar Rodriguez
author_facet Rojas, Oldemar Rodriguez
contents This paper introduces a novel approach to statistics and data analysis, departing from the conventional assumption of data residing in Euclidean space to consider a Riemannian Manifold. The challenge lies in the absence of vector space operations on such manifolds. Pennec X. et al. in their book Riemannian Geometric Statistics in Medical Image Analysis proposed analyzing data on Riemannian manifolds through geometry, this approach is effective with structured data like medical images, where the intrinsic manifold structure is apparent. Yet, its applicability to general data lacking implicit local distance notions is limited. We propose a solution to generalize Riemannian statistics for any type of data.
format Preprint
id arxiv_https___arxiv_org_abs_2405_06799
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Riemannian Statistics for Any Type of Data
Rojas, Oldemar Rodriguez
Methodology
Statistics Theory
This paper introduces a novel approach to statistics and data analysis, departing from the conventional assumption of data residing in Euclidean space to consider a Riemannian Manifold. The challenge lies in the absence of vector space operations on such manifolds. Pennec X. et al. in their book Riemannian Geometric Statistics in Medical Image Analysis proposed analyzing data on Riemannian manifolds through geometry, this approach is effective with structured data like medical images, where the intrinsic manifold structure is apparent. Yet, its applicability to general data lacking implicit local distance notions is limited. We propose a solution to generalize Riemannian statistics for any type of data.
title Riemannian Statistics for Any Type of Data
topic Methodology
Statistics Theory
url https://arxiv.org/abs/2405.06799