Semisupervised Learning Using Negative Labels

The problem of semisupervised learning has aroused considerable research interests in the past few years. Most of these methods aim to learn from a partially labeled dataset, i.e., they assume that the exact labels of some data are already known. In this paper, we propose to use a novel type of supervision information to guide the process of semisupervised learning, which indicates whether a point does not belong to a specific category. We call this kind of information negative label (NL) and propose a novel approach called NL propagation (NLP) to efficiently make use of this type of information to assist the process of semisupervised learning. Specifically, NLP assumes that nearby points should have similar class indicators. The data labels are propagated under the guidance of NL information and the geometric structure revealed by both labeled and unlabeled points, by employing some specified initialization and parameter matrices. The convergence analysis, out-of-sample extension, parameter determination, computational complexity, and relations to other approaches are presented. We also interpret the proposed approach within the framework of regularization. Promising experimental results on image, digit, spoken letter, and text classification tasks are provided to show the effectiveness of our method.