DocumentCode :
714417
Title :
Dimensionality reduction by using transductive learning and binary hierarchical trees
Author :
Dongel, Tugce ; Cevikalp, Hakan
Author_Institution :
Makine ile Ogrenme ve Bilgisayarli Goru Laboratuari, Eskisehir Osmangazi Univ., Eskisehir, Turkey
fYear :
2015
fDate :
16-19 May 2015
Firstpage :
767
Lastpage :
770
Abstract :
In this study, transductive learning and binary hierarchical decision trees are used together to find discriminative embedding (projection) directions. The projection directions returned by the proposed methodology are used for dimensionality reduction and the accuracy of nearest neighbor classification is significantly improved. We choose random classes and samples to create multiple hierarchical trees, and transductive support vector machine (TSVM) classifier is used to separate the data samples at each node of the binary hierarchical trees. The normals of the separating hyperplanes returned by the TSVM are used for dimensionality reduction. Different strategies are used to combine the projection directions coming from different hierarchical trees. In all experiments significant improvement are obtained over the nearest neighbor using full dimensionality of the input space. Since dimensionality is reduced significantly, speed of classifier has also been improved.
Keywords :
decision trees; learning (artificial intelligence); pattern classification; support vector machines; TSVM classifier; binary hierarchical decision tree; binary hierarchical tree; dimensionality reduction; discriminative embedding direction; hyperplane; multiple hierarchical tree; nearest neighbor classification; projection direction; transductive learning; transductive support vector machine classifier; Accuracy; Computer vision; Conferences; Decision trees; Fasteners; Pattern recognition; Support vector machines; binary; hierarchical decision trees; nearest neighbour classifier; transductive learning; transductive support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2015 23th
Conference_Location :
Malatya
Type :
conf
DOI :
10.1109/SIU.2015.7129941
Filename :
7129941
Link To Document :
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