Sciweavers

Share
PAKDD
2004
ACM

Spectral Energy Minimization for Semi-supervised Learning

9 years 9 months ago
Spectral Energy Minimization for Semi-supervised Learning
The use of unlabeled data to aid classification is important as labeled data is often available in limited quantity. Instead of utilizing training samples directly into semi-supervised learning, energy function incorporating the conditional probability of classification is adopted. The semi-supervised learning is posed as the optimization of both the classification energy and the cluster compactness energy. The resulting integer programming is relaxed by a semi-definite relaxation where efficient solution can be obtained. Furthermore, the spectral graph methods provide improved energy minimization via the incorporation of additional criteria. Results on UCI datasets show promising results.
Chun Hung Li, Zhi-Li Wu
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where PAKDD
Authors Chun Hung Li, Zhi-Li Wu
Comments (0)
books