تقرير
Semi-supervised Fr\'echet Regression
| العنوان: | Semi-supervised Fr\'echet Regression |
|---|---|
| المؤلفون: | Qiu, Rui, Yu, Zhou, Lin, Zhenhua |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Mathematics Statistics |
| مصطلحات موضوعية: | Mathematics - Statistics Theory, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | This paper explores the field of semi-supervised Fr\'echet regression, driven by the significant costs associated with obtaining non-Euclidean labels. Methodologically, we propose two novel methods: semi-supervised NW Fr\'echet regression and semi-supervised kNN Fr\'echet regression, both based on graph distance acquired from all feature instances. These methods extend the scope of existing semi-supervised Euclidean regression methods. We establish their convergence rates with limited labeled data and large amounts of unlabeled data, taking into account the low-dimensional manifold structure of the feature space. Through comprehensive simulations across diverse settings and applications to real data, we demonstrate the superior performance of our methods over their supervised counterparts. This study addresses existing research gaps and paves the way for further exploration and advancements in the field of semi-supervised Fr\'echet regression. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.10444 |
| رقم الأكسشن: | edsarx.2404.10444 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |