Computer Science Department
School of Computer Science, Carnegie Mellon University
Local Linear Semi-supervised Regression
Mugizi Robert Rwebangira, John Lafferty
In many machine learning application domains, obtaining labeled data is expensive but obtaining unlabeled data is much cheaper. For this reason there has been growing interest in algorithms that are able to take advantage of unlabeled data. In this report we propose an algorithm for using unlabeled data in a regression problem. The idea behind the method is to do manifold regularization using local linear estimators. This is the first extension of local linear regression to the semi-supervised setting. We present experimental results on both synthetic and real data and show that this method tends to perform better than methods which only utilize the labeled data.