A new supervised manifold learning algorithm

Abstract

In order to overcome the shortcomings of existing maniflod learning algorithm, a new supervised manifold algorithm, which improves the original algorithm and makes it more reasonably, has been proposed. Firstly, a more accurate within-class scatter matrix only with the samples belong to the same class is established to characterize the local structure of each manifold. Secondly, nearby maniflods, which can reflect the relationships of different maniflods more accurately, are selected to establish the between-class scatter matrix to characterize the discreteness of different maniflods. Finally, the Fisher criterion is used to solve the objective function and get the optimal projection direction, which can maximize the ratio of the trace of the between-class scatter matrix to the trace of the within-class scatter matrix. Experimental results demonstrate that the proposed algorithm is effective in feature extraction, leading to promising recognition performance in face recognition.