A novel semi-supervised feature extraction algorithm

Abstract

Supervised feature extraction algorithms usually require lots of labeled samples to achieve good performance. However, labeling the samples is often time-consuming and even impractical. Therefore, in this paper, a semi-supervised manifold local Fisher discriminant analysis (SMLFDA) is proposed to take advantage of unlabeled samples as well as labeled samples. The proposed algorithm utilizes local scatter matrix and manifold structure to extract the information from labeled and unlabeled samples, respectively, which significantly improves the accuracy of successive classification application when labeled samples are insufficient. In addition, an exponential form weighting coefficient is proposed to further improve the classification performance. Experiments of hyperspectral classification demonstrate the effectiveness of the proposed semi-supervised feature extraction algorithm.