Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images


During this paper, we present a graph illustration that's primarily based on the belief that information continue to exist a union of manifolds. Such a representation is predicated on sample proximities in reproducing kernel Hilbert spaces and is therefore linear in the feature space and nonlinear in the initial area. Moreover, it additionally expresses sample relationships underneath sparse and low-rank constraints, which means that the ensuing graph will have limited connectivity (sparseness) which samples belonging to the same group can be seemingly to be connected together and not with those from alternative teams (low rankness). We tend to gift this graph representation as a general representation which will be then applied to any graph-primarily based technique. Within the experiments, we have a tendency to contemplate the clustering of hyperspectral images and semi-supervised classification (one category and multiclass).

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