Nonnegative matrix factorization with Gaussian process priors
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization
Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization
Minimum Determinant Constraint for Non-negative Matrix Factorization
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Bayesian Non-negative Matrix Factorization
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery
IEEE Transactions on Signal Processing
A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing
IEEE Transactions on Signal Processing
Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior
Journal of Signal Processing Systems
IEEE Transactions on Signal Processing
Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior
Journal of Signal Processing Systems
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Hyperspectral imaging can be used in assessing the quality of foods by decomposing the image into constituents such as protein, starch, and water. Observed data can be considered a mixture of underlying characteristic spectra (endmembers), and estimating the constituents and their abundances requires efficient algorithms for spectral unmixing. We present a Bayesian spectral unmixing algorithm employing a volume constraint and propose an inference procedure based on Gibbs sampling. We evaluate the method on synthetic and real hyperspectral data of wheat kernels. Results show that our method perform as good or better than existing volume constrained methods. Further, our method gives credible intervals for the endmembers and abundances, which allows us to asses the confidence of the results.