A safe semi-supervised kernel minimum squared error algorithm

Abstract

Semi-supervised learning has received much attention in machine learning field over the past decades and a number of algorithms are proposed to improve the performance by exploiting unlabeled data. However, unlabeled data may hurt performance of semi-supervised learning in some cases. It is instinctively expected to design a reasonable strategy to safety exploit unlabeled data. To address the problem, we introduce a safe semi-supervised learning by analyzing the different characteristics of unlabeled data in supervised and semi-supervised learning. Our intuition is that unlabeled data may be risky in semi-supervised setting and the risk degree are different. Hence, we assign different weights to unlabeled data. The unlabeled data with high risk should be exploited by supervised learning and the other should be used for semi-supervised learning. In particular, we utilize Kernel Minimum Squared Error (KMSE) and Laplacian regularized KMSE (LapKMSE) for supervised and semi-supervised learning, respectively. Experimental results on several benchmark datasets illustrate the effectiveness of our algorithm.