Supervised learning on local tangent space

Authors:
Hongyu Li;Li Teng;Wenbin Chen;I-Fan Shen
Affiliations:
Department of Computer Science and Engineering, Fudan University, Shanghai, China;Department of Computer Science and Engineering, Fudan University, Shanghai, China;Department of Mathematics, Fudan University, Shanghai, China;Department of Computer Science and Engineering, Fudan University, Shanghai, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

A novel supervised learning method is proposed in this paper. It is an extension of local tangent space alignment (LTSA) to supervised feature extraction. First LTSA has been improved to be suitable in a changing, dynamic environment, that is, now it can map new data to the embedded low-dimensional space. Next class membership information is introduced to construct local tangent space when data sets contain multiple classes. This method has been applied to a number of data sets for classification and performs well when combined with some simple classifiers.