Semi-supervised discriminant analysis based on UDP regularization

We propose a semi-supervised learning algorithm for discriminant analysis, which uses the geometric structure of both labeled and unlabeled samples and perform a manifold regularization on LDA. The labeled data points provide labeling information and the unlabeled data points provide extra geometric structure information of the data, then we learn a labeling function which is as smooth as possible on the data manifold. Experiments on several face databases show the effectiveness of the algorithm.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Huining Qiu, Jian-Huang Lai, Jian Huang, Yu Chen
• Keywords: Computer Vision, Geometric Structure, ICPR 2008, Semi-supervised Learning Algorithm, Unlabeled Data Points