Semi-Supervised Learning Based on Semiparametric Regularization

Semi-supervised learning plays an important role in the recent literature on machine learning and data mining and the developed semisupervised learning techniques have led to many data mining applications in recent years. This paper addresses the semi-supervised learning problem by developing a semiparametric regularization based approach, which attempts to discover the marginal distribution of the data to learn the parametric function through exploiting the geometric distribution of the data. This learned parametric function can then be incorporated into the supervised learning on the available labeled data as the prior knowledge. Specifically, our contributions are: (1) We present a semi-supervised learning approach which incorporates the unlabeled data into the supervised learning by a parametric function learned from the whole data including the labeled and unlabeled data. The parametric function reflects the geometric structure of the marginal distribution of the data. Furthermor...

• Type: Conference
• Year: 2008
• Where: SDM
• Authors: Zhen Guo, Zhongfei (Mark) Zhang, Eric P. Xing, Christos Faloutsos
• Keywords: Data Mining, Parametric Function, SDM 2008, Semi-supervised Learning