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Oja E, Plumbley M (2004) Blind separation of positive sources by globally convergent gradient search. Neural Comput 16:1811-25 [PubMed]

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Amari SI, Cichocki A (2002) Adaptive blind signal and image processing
Cardoso JF, Laheld B (1996) Equivalent adaptive source separation. IEEE Trans Signal Proc 44:3017-3030
Charles D, Fyfe C (1998) Modelling multiple-cause structure using rectification constraints. Network 9:167-82 [PubMed]
Edelman A, Arias TA, Smith ST (1998) The geometry of algorithms with orthogonality constraints SIAM J Matrix Anal Appl 20:303-353
Fiori S (2001) A theory for learning by weight flow on Stiefel-Grassman manifold Neural Comput 13:1625-1647
Fyfe C (1994) Positive weights in interneurons Neuralcomputing: Research and applications II. Proceedings of the Third Irish Neural Networks Conference, Orchard G, ed. pp.47
Harpur GF (1997) Low entropy coding with unsupervised neural networks Unpublished doctoral disseratation
Henry RC (2002) Multivariate receptor models current practice and future trends Chemometrics And Intelligent Laboratory Systems 60:43-48
Hyvarinen A, Karunen J, Oja E (2001) Independent component analysis
Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401:788-91 [Journal] [PubMed]
Lee JS, Lee DD, Choi S, Lee DS (2001) Application of nonnegative matrix factorization to dynamic positron emission tomography Proc Intl Conf Independent Component Analysis and Signal Separation, Lee TW:Jung TP:Makeig S:Sejnowski TJ, ed. pp.629
Oja E (1983) Subspace methods of pattern recognition
Oja E (1997) The nonlinear PCA learning rule and signal separation: Mathematical analysis Neurocomputing 17:25-46
Oja E (1999) The nonlinear PCA learning rule in independent component analysis Proc ICA :143-148
Oja E, Plumbley MD (2003) Blind separation of positive sources using nonnegative ICA Proc Intl Workshop on Independent Component Analysis and Signal Separation
Paatero P, Tapper U (1994) Positive matrix factorization: A non-negative factor model with optimal utilization of error Environmetrics 5:111-126
Parra L, Spence C, Sajda P, Ziehe A, Muller KR (2000) Unmixing hyperspectral data Advances in neural information processing systems, Solla SA:Leen TK:Muller KR, ed. pp.942
Plumbley MD (2002) Conditions for nonnegative independent component analysis IEEE Signal Processing Lett 9:177-180
Plumbley MD (2003) Algorithms for non-negative independent component analysis IEEE Trans Neural Networks 14:534-543
Plumbley MD, Oja E (2004) A non-negative PCA algorithm for independent component analysis IEEE Trans Neural Networks 15:66-76
Tsuge S, Shishibori M, Kuroiwa S, Kita K (2001) Dimensionality reduction using non-negative matrix factorization for information retrieval IEEE Intl Conf Systems Man Cybernet 2:960-965
Xu L (1992) Least mean square error reconstruction principle for self-organizing neural nets Neural Netw 6:627-648
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