The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
A new concept for separability problems in blind source separation
Neural Computation
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Sparse ICA via cluster-wise PCA
Neurocomputing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Algorithms for nonnegative independent component analysis
IEEE Transactions on Neural Networks
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
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A new blind source separation method for non-negative sources based on geometrical evidences of the linear mixing model is presented. We show that the proposed method is able to find the mixing matrix as well as the original sources from an observation matrix under the assumption that for every source there is at least one instance where the underlined source is active and all the others are not. One major advantage of our proposal is that the number of sources is found automatically as being the number of extreme data in a set of points. Under the assumption mentioned above, our approach outperforms two well known implementations for NNMF BSS (ALS and multiplicative update algorithms).