关键词: 主动学习, 流形学习, 拉普拉斯图, 数据挖掘, 机器学习

Abstract: To efficiently solve remote sensing image classification problem, a new classification algorithm based on manifold structure and Support Vector Machine (SVM) was proposed. Firstly, the proposed algorithm trained the SVM with initial training set and found the samples close to the decision hyperplane, then built the manifold structure of the samples by using Laplacian graph of the selected samples. The manifold structure was applied to find the representative samples for the classifier. The experimental evaluations were conducted on the hyperspectral images, and the effectiveness of the proposed algorithm was evaluated by comparing it with other active learning techniques exiting in the literature. The experimental results on data set confirm that the algorithm has higher classification accuracy.

Key words: active learning, manifold learning, Laplacian graph, data mining, machine learning