A New Framework for Evaluating the Validity and the Performance of Binary Decisions on Manifold-Valued Data
Abstract
In this paper, we introduce a new framework that can be used for evaluating the validity and the performance of machine learning models on manifold-valued data. More particularly, two methods are detailed with theoretical properties for spherical and functional data. In a general setting, we develop a new set of procedures for nonparametric hypothesis testing on manifolds within a desired error level. These tests encompass probability distributions constrained to specific domains, which can pose significant challenges for commonly used techniques. The resulting statistical concepts are primarily characterized by computational simplicity and are grounded in relevant contexts, making them extendable to a wide range of applications. The algorithms and the theoretical analysis of the proposed methods are substantiated by many and varied experimental results on simulated and real data.
Cite
Text
Fradi and Samir. "A New Framework for Evaluating the Validity and the Performance of Binary Decisions on Manifold-Valued Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024. doi:10.1007/978-3-031-70341-6_24Markdown
[Fradi and Samir. "A New Framework for Evaluating the Validity and the Performance of Binary Decisions on Manifold-Valued Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024.](https://mlanthology.org/ecmlpkdd/2024/fradi2024ecmlpkdd-new/) doi:10.1007/978-3-031-70341-6_24BibTeX
@inproceedings{fradi2024ecmlpkdd-new,
title = {{A New Framework for Evaluating the Validity and the Performance of Binary Decisions on Manifold-Valued Data}},
author = {Fradi, Anis and Samir, Chafik},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2024},
pages = {406-421},
doi = {10.1007/978-3-031-70341-6_24},
url = {https://mlanthology.org/ecmlpkdd/2024/fradi2024ecmlpkdd-new/}
}