Conditions for nonnegative independent component analysis


We consider the noiseless linear independent component analysis problem, in the case where the hidden sources s are nonnegative. We assume that the random variables si are well grounded in that they have a nonvanishing probability density function (PDF) in the (positive) neighborhood of zero. For an orthonormal rotation y=Wx of prewhitened observations x=QAs, under certain reasonable conditions we show that y is a permutation of the s (apart from a scaling factor) if and only if y is nonnegative with probability 1. We suggest that this may enable the construction of practical learning algorithms, particularly for sparse nonnegative sources.

1 Figure or Table


Citations per Year

99 Citations

Semantic Scholar estimates that this publication has 99 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Plumbley2002ConditionsFN, title={Conditions for nonnegative independent component analysis}, author={M. D. Plumbley}, journal={IEEE Signal Processing Letters}, year={2002}, volume={9}, pages={177-180} }