open access

https://hdl.handle.net/2115/39823

Performance and Complexity of Online Kernel Algorithms

英語

2009-10-04

Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference, International Organizing Committee

Proceedings : APSIPA ASC 2009 : Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference

853-858

APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference. 4-7 October 2009. Sapporo, Japan. Oral session: Signal Processing Theory and Methods II (7 October 2009).

This paper discusses some recent advances in the development of nonlinear adaptive filtering. Specifically, it studies online kernel adaptive filters. We study the performance and complexity of a suite of kernel online algorithms from kernel recursive least square subspace (KRLSS) algorithms to kernel least mean square (KLMS) algorithms. A key to the kernel algorithms is that updating is done in the dual space via evaluation of kernels, the number of support vectors is controlled by using an information criterion, and the number of information vectors is controlled for KRLSS These algorithms have advantages in that they are nonlinear filters that are relatively easy to implement and have a number of parameters that can be adjusted to tradeoff performance for complexity.

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