We investigate a family of regression problems in a semi-supervised setting.
The task is to assign real-valued labels to a set of $n$ sample points,
provided a small training subset of $N$ labeled points. A goal of
semi-supervised learning is to take advantage of the (geometric) structure
provided by the large number of unlabeled data when assigning labels. We
consider random geometric graphs, with connection radius $\epsilon(n)$, to
represent the geometry of the data set. Functionals which model the task reward
the regularity of the estimator function and impose or reward the agreement
with the training data. Here we consider the discrete $p$-Laplacian
regularization.
We investigate asymptotic behavior when the number of unlabeled points
increases, while the number of training points remains fixed. We uncover a
delicate interplay between the regularizing nature of the functionals
considered and the nonlocality inherent to the graph constructions. We
rigorously obtain almost optimal ranges on the scaling of $\epsilon(n)$ for the
asymptotic consistency to hold. We prove that the minimizers of the discrete
functionals in random setting converge uniformly to the desired continuum
limit. Furthermore we discover that for the standard model used there is a
restrictive upper bound on how quickly $\epsilon(n)$ must converge to zero as
$n \to \infty$. We introduce a new model which is as simple as the original
model, but overcomes this restriction.

本研究探讨了半监督学习中的回归问题，以随机几何图形模拟数据几何结构，将离散的 $p$- 拉普拉斯正则化纳入模型，研究了无标记点数增加时渐近表现的性质，发现模型存在收敛性限制，提出了一个简单的模型来解决这一限制。