Concept decompositions for large sparse text data using clustering
Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
COCOON'03 Proceedings of the 9th annual international conference on Computing and combinatorics
A unified framework for semi-supervised dimensionality reduction
Pattern Recognition
A Modified Semi-Supervised Learning Algorithm on Laplacian Eigenmaps
Neural Processing Letters
Artificial Intelligence in Medicine
Semi-supervised learning by spectral mapping with label information
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
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A dimensionality reduction technique is presented for semi-supervised face recognition where image data are mapped into a low dimensional space with a spectral method. A mapping of learning data is generalized to a new datum which is classified in the low dimensional space with the nearest neighbor rule. The same generalization is also devised for regularized regression methods which work in the original space without dimensionality reduction. It is shown with experiments that the spectral mapping method outperforms the regularized regression. A modification scheme for data similarity matrices on the basis of label information and a simple selection rule for data to be labeled are also devised.