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A Derivative-Free Riemannian Powell’s Method, Minimizing Hartley-Entropy-Based ICA Contrast.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Sep2016, Vol. 27 Issue 9, p1983-1990. 8p. - Publication Year :
- 2016
-
Abstract
- Even though the Hartley-entropy-based contrast function guarantees an unmixing local minimum, the reported nonsmooth optimization techniques that minimize this nondifferentiable function encounter computational bottlenecks. Toward this, Powell’s derivative-free optimization method has been extended to a Riemannian manifold, namely, oblique manifold, for the recovery of quasi-correlated sources by minimizing this contrast function. The proposed scheme has been demonstrated to converge faster than the related algorithms in the literature, besides the impressive source separation results in simulations involving synthetic sources having finite-support distributions and correlated images. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 27
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 117596579
- Full Text :
- https://doi.org/10.1109/TNNLS.2015.2464157