Semi-supervised Fr\'echet Regression

arXiv:2404.10444v1 Announce Type: cross
Abstract: 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 …

abstract acquired arxiv costs cs.lg feature graph instances knn labels math.st non-euclidean novel paper regression semi-supervised stat.ml stat.th type