Abstract Traditional manifold learning methods need a large number of training samples. All samples are regarded as a manifold and then discriminative features are extracted for practical application such as classification. But in many situations, only one sample is existed during the training phasesince there are not enough training samples. Therefore, frame bundle connection learning method is presented and a multi-manifold structure is constructed. Besides,intermanifold and intramanifold features are extracted to get more discriminative information to solve the problem. When dealing with the multi-manifold structure, learning models of two subspaces based on frame bundle are used to project the data in high-dimensional space to horizontal space for maximizing margins of different manifolds. Simultaneously, the data structure is maintained with the same manifold in the vertical space. Finally, a simulation experiment is presented to prove the validity of the proposed algorithm.
2015, Vol. 28 
