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Hyperspectral image dimensionality reduction with graph-based approaches is considered. With available labeled samples, a graph can be formed with these samples by constructing an affinity matrix through their sparse or collaborative representations. In addition, sparse or collaborative representation can be done using within-class samples, resulting in block-sparse representation, although within...
In previous work, a sparse graph-based discriminant analysis was proposed for when labeled samples are available. Although an affinity-based graph itself may not necessarily enhance the disciminant power, the discriminant power can truly be improved when an affinity matrix is retrieved from labeled samples. Additionally, a sparsity-preserving graph has been demonstrated to be capable of providing...
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