### Abstract

We propose a novel kernel adaptive filtering algorithm that selectively updates a few coefficients at each iteration by projecting the current filter onto the zero instantaneous-error hyperplane along a certain time-dependent affine subspace. Coherence is exploited for selecting the coefficients to be updated as well as for measuring the novelty of new data. The proposed algorithm is a natural extension of the normalized kernel least mean squares algorithm operating iterative hyperplane projections in a reproducing kernel Hilbert space. The proposed algorithm enjoys low computational complexity. Numerical examples indicate high potential of the proposed algorithm.

Original language | English |
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Title of host publication | Proceedings of the 20th European Signal Processing Conference, EUSIPCO 2012 |

Pages | 2183-2187 |

Number of pages | 5 |

Publication status | Published - 2012 Nov 27 |

Externally published | Yes |

Event | 20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest, Romania Duration: 2012 Aug 27 → 2012 Aug 31 |

### Publication series

Name | European Signal Processing Conference |
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ISSN (Print) | 2219-5491 |

### Conference

Conference | 20th European Signal Processing Conference, EUSIPCO 2012 |
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Country | Romania |

City | Bucharest |

Period | 12/8/27 → 12/8/31 |

### Keywords

- kernel adaptive filter
- normalized kernel least mean square algorithm
- projection algorithms
- reproducing kernel Hilbert space

### ASJC Scopus subject areas

- Signal Processing
- Electrical and Electronic Engineering

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## Cite this

*Proceedings of the 20th European Signal Processing Conference, EUSIPCO 2012*(pp. 2183-2187). [6333933] (European Signal Processing Conference).