Speaker: Mugizi Robert Rwebangira, Howard University

Title: Local Linear Semi-supervised Regression


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 work 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 an extension of local linear regression to the semi-supervised setting. We present experimental results on both synthetic and real data and identify cases that are particularly well suited to the method's assumptions.