### Abstract

The contributions of this paper are twofold. The first is to give theoretical motivation for whitening in the recently proposed adaptive filtering algorithm named the Krylov-proportionate normalized least-mean-square (KPNLMS) algorithm. The second is to present the details of whitening in KPNLMS (In the original work of KPNLMS, the whitening procedure is mentioned but is not described in detail). An interesting connection among the transform-domain adaptive filter (TDAF), proportionate normalized least-mean-square (PNLMS), and KPNLMS algorithms is also provided. Numerical examples demonstrate that KPNLMS drastically outperforms TDAF especially in noisy situations.

Original language | English |
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Title of host publication | Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |

Pages | 315-320 |

Number of pages | 6 |

DOIs | |

Publication status | Published - 2008 Dec 1 |

Externally published | Yes |

Event | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico Duration: 2008 Oct 16 → 2008 Oct 19 |

### Publication series

Name | Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |
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### Other

Other | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |
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Country | Mexico |

City | Cancun |

Period | 08/10/16 → 08/10/19 |

### Keywords

- Adaptive filter
- Krylov subspace
- Proportionate NLMS
- Whitening

### ASJC Scopus subject areas

- Artificial Intelligence
- Software
- Electrical and Electronic Engineering

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## Cite this

*Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008*(pp. 315-320). [4685499] (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008). https://doi.org/10.1109/MLSP.2008.4685499