A unified framework for semi-supervised dimensionality reduction. (English) Zbl 1154.68501

Summary: In practice, many applications require a dimensionality reduction method to deal with the partially labeled problem. In this paper, we propose a semi-supervised dimensionality reduction framework, which can efficiently handle the unlabeled data. Under the framework, several classical methods, such as principal component analysis, linear discriminant analysis, maximum margin criterion, locality preserving projections and their corresponding kernel versions can be seen as special cases. For high-dimensional data, we can give a low-dimensional embedding result for both discriminating multi-class sub-manifolds and preserving local manifold structure. Experiments show that our algorithms can significantly improve the accuracy rates of the corresponding supervised and unsupervised approaches.


68T10 Pattern recognition, speech recognition
68T05 Learning and adaptive systems in artificial intelligence
Full Text: DOI


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