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Experiments in high-energy astroparticle physics produce large amounts of data as continuous high-volume streams. Gaining insights from the observed data poses a number of challenges to data analysis at various steps in the analysis chain of the experiments. Machine learning methods have already cleaved their way selectively at some particular stages of the(More)
Lagrange constrained neural network (LCNN) was an unsupervised technique that can simultaneously estimate the endmembers and their abundance fractions in a remotely sensed image without any prior information. The network outputs corresponded to the estimated abundance fraction images (AFI), which displayed the distribution of the endmember materials in an(More)
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