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

Date

2016-07-01

Author

Sakarya, Ufuk

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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.

Subject Keywords

Dimension reduction, Global-local maximum margin criterion, Handwritten digit recognition, Object recognition, Hyperspectral image classification

URI

https://hdl.handle.net/11511/63654

Journal

SIGNAL IMAGE AND VIDEO PROCESSING

DOI

https://doi.org/10.1007/s11760-015-0838-5

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Citation Formats

U. Sakarya, “Dimension reduction using global and local pattern information-based maximum margin criterion,” SIGNAL IMAGE AND VIDEO PROCESSING, pp. 903–909, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/63654.