A non-negative sparse semi-supervised dimensionality reduction algorithm for hyperspectral data

A non-negative sparse semi-supervised dimensionality reduction algorithm is proposed for hyperspectral data by making adequate use of a few labeled samples and a large number of unlabeled samples. The objective function of the proposed algorithm consists of two terms: (1) a discriminant item is designed to analyze a few labeled samples from the global viewpoint, which can assess the separability between surface objects; (2) a regularization term is used to build a non-negative sparse representation graph based on the unlabeled samples, which can adaptively find an adjacency graph for each sample and then find valuable samples with huge information volume from the original hyperspectral data. Based on the objective function and the maximum margin criterion, a dimensionality reduction algorithm, the non-negative sparse semi-supervised maximum margin algorithm, is proposed. Experimental results on the ROSIS University and AVIRIS 92AV3C hyperspectral data sets show that the proposed algorithm can effectively utilize the unlabeled samples to achieve higher overall classification accuracy and Kappa coefficient when compared with some representative supervised, unsupervised and semi-supervised dimensionality reduction algorithms.