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Average neighborhood margin maximization projection with smooth regularization for face recognition
- Source :
- 2008 International Conference on Machine Learning and Cybernetics.
- Publication Year :
- 2008
- Publisher :
- IEEE, 2008.
-
Abstract
- Dimensionality reduction is among the keys in many fields, most of the traditional method can be categorized as local or global ones. In this paper, we consider the dimension reduction problem with prior information is available, namely, semi-supervised dimension reduction. A new dimension reduction method that can explore both the labeled and unlabeled information in the dataset is proposed. The labeled data points are used to maximize the separability between different classes and the unlabeled data points are used to estimate the intrinsic geometric structure of the data. Specifically, we aim to learn a discriminant function which is as smooth as possible on the data manifold. The target optimization problem involved can be solved efficiently with eigenvalue decomposition. Experimental results on several datasets demonstrate the effectiveness of our method.
- Subjects :
- Optimization problem
business.industry
Dimensionality reduction
Pattern recognition
Semi-supervised learning
Linear discriminant analysis
Regularization (mathematics)
ComputingMethodologies_PATTERNRECOGNITION
Data point
Principal component analysis
Artificial intelligence
business
Mathematics
Data reduction
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2008 International Conference on Machine Learning and Cybernetics
- Accession number :
- edsair.doi...........99b2843edaf4350148131de63b7eb6c7