Learning with Unlabeled Data

of thesis entitled: Learning with Unlabeled Data Submitted by XU, Zenglin for the degree of Doctor of Philosophy at The Chinese University of Hong Kong in January 2009 We consider the problem of learning from both labeled and unlabeled data through the analysis on the quality of the unlabeled data. Usually, learning from both labeled and unlabeled data is regarded as semi-supervised learning, where the unlabeled data and the labeled data are assumed to be generated from the same distribution. When this assumption is not satisfied, new learning paradigms are needed in order to effectively explore the information underneath the unlabeled data. This thesis consists of two parts: the first part analyzes the fundamental assumptions of semi-supervised learning and proposes a few efficient semi-supervised learning models; the second part discusses three learning frameworks in order to deal with the case that unlabeled data do not satisfy the conditions of semisupervised learning. In the first part, we deal with the unlabeled data that are in good quality and follow the conditions of semi-supervised learning. Firstly, we present a novel method for Transductive Support Vector Machine (TSVM) by relaxing the unknown labels to the continuous variables and reducing the non-convex optimization problem to a convex semi-definite programming problem. In contrast to the previous relaxation method which involves O(n2) free parameters in the semi-definite matrix, our method takes