Supervised learning 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:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Year:
2005

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Abstract

Supervised local tangent space alignment is proposed for data classification in this paper. It is an extension of local tangent space alignment, for short, LTSA, from unsupervised to supervised learning. Supervised LTSA is a supervised dimension reduction method. It make use of the class membership of each data to be trained in the case of multiple classes, to improve the quality of classification. Furthermore we present how to determine the related parameters for classification and apply this method to a number of artificial and realistic data. Experimental results show that supervised LTSA is superior for classification to other popular methods of dimension reduction when combined with simple classifiers such as the k-nearest neighbor classifier.