K-NN active learning under local smoothness assumption
This work addresses active learning efficiency for machine learning practitioners by offering a more generally applicable method, though it appears incremental as it builds on existing smoothness assumptions.
The authors tackled the problem of active learning convergence rates by designing a k-NN-based algorithm that achieves better convergence than passive learning under a customized smoothness assumption, avoiding the strong density assumption for broader applicability.
There is a large body of work on convergence rates either in passive or active learning. Here we first outline some of the main results that have been obtained, more specifically in a nonparametric setting under assumptions about the smoothness of the regression function (or the boundary between classes) and the margin noise. We discuss the relative merits of these underlying assumptions by putting active learning in perspective with recent work on passive learning. We design an active learning algorithm with a rate of convergence better than in passive learning, using a particular smoothness assumption customized for k-nearest neighbors. Unlike previous active learning algorithms, we use a smoothness assumption that provides a dependence on the marginal distribution of the instance space. Additionally, our algorithm avoids the strong density assumption that supposes the existence of the density function of the marginal distribution of the instance space and is therefore more generally applicable.