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However, the performance of these methods is low due to the following limitations: (1) ignore the unlabeled samples; (2) only exploit the global Euclidean structure and (3) not taking advantage of the various useful aspects of the object. In order to solve the first problem, we propose a graph-based semisupervised learning (GSEL), which can add positive samples and construct balanced sets. With the second problem, we propose a manifold learning for dimensional reduction (MAL), which exploits the geometric properties of the manifold data. With the third problem, we propose a combination of classifiers by aspect (CCA), which exploits the various useful aspects of the object. 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