Semi-supervised kernel learning based optical image recognition

This paper is to propose semi-supervised kernel learning based optical image recognition, called Semi-supervised Graph-based Global and Local Preserving Projection (SGGLPP) through integrating graph construction with the specific DR process into one unified framework. SGGLPP preserves not only the positive and negative constraints but also the local and global structure of the data in the low dimensional space. In SGGLPP, the intrinsic and cost graphs are constructed using the positive and negative constraints from side-information and k nearest neighbor criterion from unlabeled samples. Moreover, kernel trick is applied to extend SGGLPP called KSGGLPP by on the performance of nonlinear feature extraction. Experiments are implemented on UCI database and two real image databases to testify the feasibility and performance of the proposed algorithm.