A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing
IEEE Transactions on Signal Processing
Maximum contrast analysis for nonnegative blind source separation
Computers & Mathematics with Applications
Initialization of nonnegative matrix factorization with vertices of convex polytope
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Sparse and unique nonnegative matrix factorization through data preprocessing
The Journal of Machine Learning Research
The CAM software for nonnegative blind source separation in R-Java
The Journal of Machine Learning Research
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Although significant efforts have been made in developing nonnegative blind source separation techniques, accurate separation of positive yet dependent sources remains a challenging task. In this paper, a joint correlation function of multiple signals is proposed to reveal and confirm that the observations after nonnegative mixing would have higher joint correlation than the original unknown sources. Accordingly, a new nonnegative least-correlated component analysis (n{\rm LCA}) method is proposed to design the unmixing matrix by minimizing the joint correlation function among the estimated nonnegative sources. In addition to a closed-form solution for unmixing two mixtures of two sources, the general algorithm of n{\rm LCA} for the multisource case is developed based on an iterative volume maximization (IVM) principle and linear programming. The source identifiability and required conditions are discussed and proven. The proposed n{\rm LCA} algorithm, denoted by n{\rm LCA\hbox{-}IVM}, is evaluated with both simulation data and real biomedical data to demonstrate its superior performance over several existing benchmark methods.