Semi-Supervised Smoothing For Large Data Problems

This book chapter is a description of some recent developments in non-parametric semi-supervised regression and is intended for someone with a background in statistics, computer science, or data sciences who is familiar with local kernel smoothing (Hastie et al., The elements of statistical learning (data mining, inference and prediction), chapter 6. Springer, Berlin, 2009). In many applications, response data often require substantially more effort to obtain than feature data. Semi-supervised learning approaches are designed to explicitly train a classifier or regressor using all the available responses and the full feature data. This presentation is focused on local kernel regression methods in semi-supervised learning and provides a good starting point for understanding semi-supervised methods in general.