Semisupervised Linear Discriminant Analysis Based on Pairwise Constraint Propagation for Hyperspectral Images

As a classical supervised dimensionality reduction (DR) method, linear discriminant analysis (LDA) has been developed for many variants. However, it is not applicable to the case that labeled samples are scarce and unlabeled samples are in large quantity, which always happens in the real world. In this letter, we propose a novel technique format termed semisupervised LDA based on pairwise constraint propagation (SLDA-PCP) for hyperspectral images (HSIs). The basic idea of this method is to use a specially designed PCP technique to propagate label information from the labeled samples to the unlabeled samples. In addition, an extended LDA format to learn the optimal projection vectors according to the newly obtained label information is also created. Comprehensive experiments on two HSIs show that our SLDA-PCP performs better than some state-of-the-art semisupervised DR methods.