Referenced Items are not available for this document.
This brief tackles the problem of learning over the complex-valued matrix-hypersphere Sn,pα(C). The developed learning theory is formulated in terms of Riemannian-gradient-based optimization of a regular criterion function and is implemented by a geodesic-stepping method. The stepping method is equipped with a geodesic-search sub-algorithm to compute the optimal learning stepsize at any step. Numerical results show the effectiveness of the developed learning method and of its implementation.
Published in:
Neural Networks, IEEE Transactions on
(Volume:22
,
Issue:
12
)
Date of Publication: Dec. 2011
- Page(s):
- 2132 - 2138
- ISSN :
- 1045-9227
- INSPEC Accession Number:
- 12409277
- Digital Object Identifier :
- 10.1109/TNN.2011.2168537
- Product Type:
- Journals & Magazines
- Date of Publication :
- 06 October 2011
- Date of Current Version :
- 12 December 2011
- Issue Date :
- Dec. 2011
- Sponsored by :
- IEEE Computational Intelligence Society
- PubMed ID :
- 21984497
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
