TY - GEN

T1 - Constrained adaptive learning in reproducing kernel hilbert spaces

T2 - 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

AU - Slavakis, Konstantinos

AU - Theodoridis, Sergios

AU - Yamada, Isao

PY - 2008/12/1

Y1 - 2008/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=58049158397&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=58049158397&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2008.4685451

DO - 10.1109/MLSP.2008.4685451

M3 - Conference contribution

AN - SCOPUS:58049158397

SN - 9781424423767

T3 - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

SP - 32

EP - 37

BT - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Y2 - 16 October 2008 through 19 October 2008

ER -