TY - JOUR
T1 - On efficient large margin semisupervised learning
T2 - Method and theory
AU - Wang, Junhui
AU - Shen, Xiaotong
AU - Pan, Wei
PY - 2009/1
Y1 - 2009/1
N2 - In classification, semisupervised learning usually involves a large amount of unlabeled data with only a small number of labeled data. This imposes a great challenge in that it is difficult to achieve good classification performance through labeled data alone. To leverage unlabeled data for enhancing classification, this article introduces a large margin semisupervised learning method within the framework of regularization, based on an efficient margin loss for unlabeled data, which seeks efficient extraction of the information from unlabeled data for estimating the Bayes decision boundary for classification. For implementation, an iterative scheme is derived through conditional expectations. Finally, theoretical and numerical analyses are conducted, in addition to an application to gene function prediction. They suggest that the proposed method enables to recover the performance of its supervised counterpart based on complete data in rates of convergence, when possible.
AB - In classification, semisupervised learning usually involves a large amount of unlabeled data with only a small number of labeled data. This imposes a great challenge in that it is difficult to achieve good classification performance through labeled data alone. To leverage unlabeled data for enhancing classification, this article introduces a large margin semisupervised learning method within the framework of regularization, based on an efficient margin loss for unlabeled data, which seeks efficient extraction of the information from unlabeled data for estimating the Bayes decision boundary for classification. For implementation, an iterative scheme is derived through conditional expectations. Finally, theoretical and numerical analyses are conducted, in addition to an application to gene function prediction. They suggest that the proposed method enables to recover the performance of its supervised counterpart based on complete data in rates of convergence, when possible.
KW - Classification
KW - Difference convex programming
KW - Nonconvex minimization
KW - Regulanzation
KW - Support vectors
UR - http://www.scopus.com/inward/record.url?scp=64149104410&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=64149104410&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:64149104410
SN - 1532-4435
VL - 10
SP - 719
EP - 742
JO - Journal of Machine Learning Research
JF - Journal of Machine Learning Research
ER -