Affiliations: [a] School of Computer Science and Engineering, Hebei University of Technology, Tianjin, China
| [b] Hebei Province Bigdata Computation Key Laboratory, Tianjin, China
Abstract: In image recognition, the within-class matrix in some multi-manifold learning algorithms is singular, which affects the recognition effectiveness. To solve the problem, a supervised multi-manifold learning method is proposed, which extracts multi-manifold features of images by maximizing the between-class Laplacian graph and hides the minimization of the within-class Laplacian graph in the maximization of the between-class Laplacian graph by introducing the class labels. This method provides an explicit mapping between the high dimensional images and the low dimensional features, which can project samples out of the training set into the low dimensional space and also overcomes the singular problem of the within-class matrix. The proposed algorithm is tested on the pavement distress images, ORL and FERET face images. Experiments show that the recognition accuracy is greatly improved, and the dimension of the low dimensional features is determined. And the influence of Euclidean distance and the angle cosine distance on the recognition results is compared by using KNN.
