关键词: 极限学习机, 流形学习, 近邻保持嵌入, 几何结构

Abstract: Extreme learning machine is widely used in the field of face recognition. The traditional extreme learning algorithm easily leads to the problem of insufficient learning process due to training on a small number of labeled samples. At the same time, the intrinsic geometry of the sample is often ignored in the learning process, which affects its classification ability for face recognition. Inspired by the idea of manifold learning, a neighborhood preserving extreme learning algorithm is proposed. The algorithm preserves the most essential structure of data and discriminant information of intra-class data, and minimizes the intra-class divergence matrix to improve the classification performance of the extreme learning machine. The experimental  results on face datasets show that the algorithm has a higher accuracy of face recognition than other algorithms and is more effective in classification and recognition.

Key words: extreme learning machine, manifold learning, neighborhood preserving embedding, geometric structure