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Nonnegative matrix factorization with data-guided constraints

Hyperspectral unmixing aims to estimate a set of endmembers and their corresponding percentages in pixels. NMF and its extensions with various constraints have been widely applied to hyperspectral unmixing. L1/2 regularizer and L2 regularizer can be added into NMF to enforce sparseness and smoothness respectively. In practice, an rigion in hyperspectral image may possesses different sparse level across locations. It remains a problem how to impose constraints accordingly when the level of sparse varies. We propose a novel nonnegative matrix factorization with data-guided constraints (DGC-NMF). The DGC-NMF assigns sparseness or smoothness constraints on abundance of each pixel individually according to their mixed level. Experiments on the synthetic data validate the proposed algorithm.