Dimension reduction using global and local pattern information-based maximum margin criterion

Dimension reduction is an important research area in pattern recognition when dealing with high-dimensional data. In this paper, a novel supervised dimension reduction approach is introduced for classification. Advantages of using not only global pattern information but also local pattern information are examined in the maximum margin criterion framework. Experimental comparative results in object recognition, handwritten digit recognition, and hyperspectral image classification are presented. According to the experimental results, the proposed method can be a valuable choice for dimension reduction when considering the difficulty of obtaining training samples for some applications.