Constrained adaptive learning in reproducing kernel hilbert spaces: The beamforming paradigm

Konstantinos Slavakis, Sergios Theodoridis, Isao Yamada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel framework for constrained adaptive learning in Reproducing Kernel Hilbert Spaces (RKHS). A low complexity algorithmic solution is established. Constraints that encode a-priori information and several design specifications take the form of multiple intersecting closed convex sets. A cost function and the training data stream create a sequence of closed convex sets in the RKHS. The resulting recursive solution generates a sequence of estimates which converges to such an infinite intersection of closed convex sets. A time-adaptive beamforming task in an RKHS, rich in constraints, is also established. The numerical results show that the proposed method exhibits a significant improvement in resolution, when compared to the classical linear solution, and outperforms a recently unconstrained online kernel-based regression technique.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pages32-37
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico
Duration: Oct 16 2008Oct 19 2008

Publication series

NameProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Other

Other2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
CountryMexico
CityCancun
Period10/16/0810/19/08

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    Slavakis, K., Theodoridis, S., & Yamada, I. (2008). Constrained adaptive learning in reproducing kernel hilbert spaces: The beamforming paradigm. In Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 (pp. 32-37). [4685451] (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008). https://doi.org/10.1109/MLSP.2008.4685451