@inproceedings{41f1f52a654b40dba26c7f5138afa1f3,
title = "Density-dependent quantized kernel least mean square",
abstract = "Kernel least mean square is a simple and effective adaptive algorithm, but dragged by its unlimited growing network size. Many schemes have been proposed to reduce the network size, but few takes the distribution of the input data into account. Input data distribution is generally important in view of both model sparsification and generalization performance promotion. In this paper, we introduce an online density-dependent vector quantization scheme, which adopts a shrinkage threshold to adapt its output to the input data distribution. This scheme is then incorporated into the quantized kernel least mean square (QKLMS) to develop a density-dependent QKLMS (DQKLMS). Experiments on static function estimation and short-term chaotic time series prediction are presented to demonstrate the desirable performance of DQKLMS.",
keywords = "Density-dependent, Kernel adaptive filter, Quantized kernel least mean square, Vector quantization",
author = "Bao Xi and Lei Sun and Badong Chen and Jianji Wang and Nanning Zheng and Pr{\'i}ncipe, {Jos{\'e} C.}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Joint Conference on Neural Networks, IJCNN 2016 ; Conference date: 24-07-2016 Through 29-07-2016",
year = "2016",
month = oct,
day = "31",
doi = "10.1109/IJCNN.2016.7727657",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3564--3569",
booktitle = "2016 International Joint Conference on Neural Networks, IJCNN 2016",
address = "United States",
}
