Signal, Image and Video Processing

, Volume 13, Issue 1, pp 171–178 | Cite as

Semi-supervised dimension reduction approaches integrating global and local pattern information

  • Ufuk SakaryaEmail author
Original Paper


Dimension reduction is an important research area in pattern recognition. Use of both supervised and unsupervised data can be an advantage in the case of lack of labeled training data. Moreover, use of both global and local pattern information can contribute classification performances. Therefore, four important primary components are essential to design a well-performed semi-supervised dimension reduction approach: global pattern modeling by a supervised manner, local pattern modeling by a supervised manner, global pattern modeling by an unsupervised manner, and local pattern modeling by an unsupervised manner. These primary components are integrated into two proposed methods. The first is the semi-supervised global–local linear discriminant analysis, and the second is the semi-supervised global–local maximum margin criterion. The proposed methods are examined in object recognition and hyperspectral image classification. According to the experimental results, the promising results are obtained against to comparative semi-supervised methods.


Dimension reduction Semi-supervised global–local linear discriminant analysis Semi-supervised global–local maximum margin criterion Object recognition Hyperspectral image classification 


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.TÜBİTAK UZAY (The Scientific and Technological Research Council of Turkey, Space Technologies Research Institute)ODTÜ YerleşkesiAnkaraTurkey

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