Vector Quantile Regression on Manifolds
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917584452976640 |
|---|---|
| author | Pegoraro, Marco Vedula, Sanketh Rosenberg, Aviv A. Tallini, Irene Rodolà, Emanuele Bronstein, Alex M. |
| author_facet | Pegoraro, Marco Vedula, Sanketh Rosenberg, Aviv A. Tallini, Irene Rodolà, Emanuele Bronstein, Alex M. |
| contents | Quantile regression (QR) is a statistical tool for distribution-free estimation of conditional quantiles of a target variable given explanatory features. QR is limited by the assumption that the target distribution is univariate and defined on an Euclidean domain. Although the notion of quantiles was recently extended to multi-variate distributions, QR for multi-variate distributions on manifolds remains underexplored, even though many important applications inherently involve data distributed on, e.g., spheres (climate and geological phenomena), and tori (dihedral angles in proteins). By leveraging optimal transport theory and c-concave functions, we meaningfully define conditional vector quantile functions of high-dimensional variables on manifolds (M-CVQFs). Our approach allows for quantile estimation, regression, and computation of conditional confidence sets and likelihoods. We demonstrate the approach's efficacy and provide insights regarding the meaning of non-Euclidean quantiles through synthetic and real data experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_01037 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Vector Quantile Regression on Manifolds Pegoraro, Marco Vedula, Sanketh Rosenberg, Aviv A. Tallini, Irene Rodolà, Emanuele Bronstein, Alex M. Methodology Machine Learning Quantile regression (QR) is a statistical tool for distribution-free estimation of conditional quantiles of a target variable given explanatory features. QR is limited by the assumption that the target distribution is univariate and defined on an Euclidean domain. Although the notion of quantiles was recently extended to multi-variate distributions, QR for multi-variate distributions on manifolds remains underexplored, even though many important applications inherently involve data distributed on, e.g., spheres (climate and geological phenomena), and tori (dihedral angles in proteins). By leveraging optimal transport theory and c-concave functions, we meaningfully define conditional vector quantile functions of high-dimensional variables on manifolds (M-CVQFs). Our approach allows for quantile estimation, regression, and computation of conditional confidence sets and likelihoods. We demonstrate the approach's efficacy and provide insights regarding the meaning of non-Euclidean quantiles through synthetic and real data experiments. |
| title | Vector Quantile Regression on Manifolds |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2307.01037 |