Maximum-distance nonnegative matrix factorization for unmixing highly mixed grain-size distribution data
MAD-NMF is a method for unmixing highly mixed grain-size distribution data using nonnegative matrix factorization. It introduces a negative distance regularization term that pushes estimated end members further apart, which outperforms existing methods like AnalySize in recovering true structures from complex, mixed datasets.