DMCA
Semi-supervised Discriminant Analysis Based on UDP Regularization
| Citations: | 3 - 0 self |
BibTeX
@MISC{Qiu_semi-superviseddiscriminant,
author = {Huining Qiu and Jianhuang Lai and Jian Huang and Yu Chen},
title = {Semi-supervised Discriminant Analysis Based on UDP Regularization},
year = {}
}
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Abstract
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. 1
Keyphrases
semi-supervised discriminant analysis udp regularization data point unlabeled data point discriminant analysis semi-supervised learning algorithm data manifold labeling function extra geometric structure information geometric structure unlabeled sample several face database manifold regularization
