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Regularized Sparse Band Selection via Learned Pairwise Agreement
2020
IEEE Access
Desired by sparse subset learning, in this paper, a hyperspectral band selection method via pairwise band agreement with spatial-spectral graph regularier, referred as Regularized Band Selection via Learned Pairwise Agreement (RBS-LPA), was proposed. The process was formulated as a graph-regularized row-sparse constrained optimization problem, which select a few representative bands to code the all bands based on the learned pairwise band agreement. In RBS-LPA, a spatial-spectral informative
doi:10.1109/access.2020.2971556
fatcat:72u7qlvq75cpnatvv773xlpxcq