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...

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Bibliographic Details
Main Authors: Huining Qiu, Jianhuang Lai, Jian Huang, Yu Chen
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Face
Feature extraction
Image databases
Labeling
Linear discriminant analysis
Pattern recognition
Performance analysis
Semisupervised learning
Sun
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Summary: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.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2008.4761802