@inproceedings{becbb5d9ec094146b4f9f392f604916f,
title = "Quaternion-Valued Adaptive Filtering via Nesterov's Extrapolation",
abstract = "A new quaternion-valued adaptive filtering algorithm based on extrapolated weight methods is proposed. The proposed algorithm belongs to the class of conjugate direction algorithms [1]. This class of extrapolation (momentum) based algorithms is preferred to RLS-based algorithms when the matrix inversion should be avoided, e.g. in the case of non-vector signals, sparse signals or non-stationary signals. This paper introduces Nesterov's optimal gradient methods in widely linear quaternion adaptive filtering. The resulting class of algorithm is shown to both have similar computational complexity and comparable performance to WLQRLS; however, the proposed method is more stable and outperforms WLQRLS in the non-stationary case.",
keywords = "Least mean square, Nesterov's gradient, quaternions, recursive least squares gradient, widely linear model",
author = "Thiernithi Variddhisai and Min Xiang and Douglas, \{Scott C.\} and Mandic, \{Danilo P.\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",
year = "2019",
month = may,
doi = "10.1109/ICASSP.2019.8682433",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4868--4872",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",
address = "美国",
}