An Efficient Semi-Supervised Classifier Based on Block-Polynomial Mapping

An Efficient Semi-Supervised Classifier Based on Block-Polynomial Mapping In this paper, we propose a block-polynomial mapping for image feature learning, which can be efficiently represented by the matrix Khatri-Rao product. The block-polynomial mapping not only captures the local discriminative information within the image structure, but is also much more efficient than the traditional kernel mapping. Moreover, we embed the proposed mapping into the manifold regularization framework for semi-supervised image classification. Experimental results demonstrate that, while maintaining a comparable classification accuracy, the proposed algorithm performs much more efficient than the state-of-the-art methods.