Descent Algorithms on Oblique Manifold for Source-Adaptive ICA Contrast

@article{Selvan2012DescentAO,
  title={Descent Algorithms on Oblique Manifold for Source-Adaptive ICA Contrast},
  author={S. Easter Selvan and Umberto Amato and Kyle A. Gallivan and Chunhong Qi and Maria Francesca Carfora and Michele Larobina and Bruno Alfano},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2012},
  volume={23},
  pages={1930-1947}
}
A Riemannian manifold optimization strategy is proposed to facilitate the relaxation of the orthonormality constraint in a more natural way in the course of performing independent component analysis (ICA) that employs a mutual information-based source-adaptive contrast function. Despite the extensive development of manifold techniques catering to the orthonormality constraint, only a limited number of works have been dedicated to oblique manifold (OB) algorithms to intrinsically handle the… CONTINUE READING

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