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

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.