Supervised local tangent space alignment for classification

Authors:
Hongyu Li;Wenbin Chen;I-Fan Shen
Affiliations:
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:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

Citing 1
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Supervised learning for classification

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Abstract

Supervised local tangent space alignment (SLTSA) is an extension of local tangent space alignment (LTSA) to supervised feature extraction. Two algorithmic improvements are made upon LTSA for classification. First a simple technique is proposed to map new data to the embedded low-dimensional space and make LTSA suitable in a changing, dynamic environment. Then SLTSA is introduced to deal with data sets containing multiple classes with class membership information.