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In this letter, we propose an improved locally linear embedding (LLE) method based on robust spatial information (named RSLLE) for hyperspectral data dimensionality reduction. It explores and takes full account of the complexity of the spatial information for LLE. In RSLLE, when searching for spectral neighbors, a kind of spectral–spatial distance is used instead of the distance between two individual...
In this paper, we present a modified self-training semi-supervised SVM algorithm. In order to demonstate its validity and effectiveness, we carry out some experimentswhich prove that our method is better than the former algorithm. Using our modified self-training semi-supervised SVM algorithm, we can save much time for lableling the unlabelled data.
In order to improve the generalization performance of support vector machine (SVM), a support vector machine ensembling method based on independent component analysis (ICA) and fuzzy kernel clustering (FKC) was proposed. The ICA emphasizes the independence between the data characteristics and can effectively obtain a series of independent features, the performance of single SVM can be improved when...
Linear discrimination analysis (LDA) is one of the most popular feature extraction and classifier design techniques. It maximizes the Fisher-ratio between between-class scatter matrix and within-class scatter matrix under a linear transformation, and the transformation is composed of the generalized eigenvectors of them. However, Fisher criterion itself can not decide the optimum norm of transformation...
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