Hou Chenping, Nie Feiping, Wang Fei, Zhang Changshui, Wu Yi
Department of Mathematics and System Science, National University of Defense Technology, Changsha, Hunan 410073, China.
IEEE Trans Neural Netw. 2011 Mar;22(3):420-32. doi: 10.1109/TNN.2010.2099237. Epub 2011 Jan 13.
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.
半监督学习问题在过去几年中引起了相当大的研究兴趣。这些方法大多旨在从部分标记的数据集中进行学习，也就是说，它们假定某些数据的准确标签已经已知。在本文中，我们建议使用一种新型的监督信息来指导半监督学习过程，该信息表明一个点是否不属于特定类别。我们将这种信息称为负标签（NL），并提出了一种名为NL传播（NLP）的新方法，以有效地利用此类信息来辅助半监督学习过程。具体而言，NLP假定附近的点应该具有相似的类别指标。通过采用一些指定的初始化和参数矩阵，数据标签在NL信息以及标记点和未标记点所揭示的几何结构的指导下进行传播。本文给出了收敛性分析、样本外扩展、参数确定、计算复杂度以及与其他方法的关系。我们还在正则化框架内解释了所提出的方法。提供了在图像、数字、语音字母和文本分类任务上的有前景的实验结果，以证明我们方法的有效性。
