The Nearest-Instance-Centroid-Estimation Kernel Recursive Least Squares Algorithms

The nearest-instance-centroid-estimation kernel least mean-square (NICE-KLMS) algorithm has been proposed to balance the time and space requirements in kernel adaptive filters. However, the minimum mean square error (MMSE) criterion used in NICE-KLMS leads to performance degradation in some nonlinea...

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Published in:IEEE transactions on circuits and systems. II, Express briefs Vol. 67; no. 7; pp. 1344 - 1348
Main Authors: Zhang, Haonan, Wang, Lin, Zhang, Tao, Wang, Shiyuan
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive filters
Algorithms
Centroids
Circuits and systems
Clustering algorithms
Computer simulation
Dictionaries
Kernel
kernel recursive least squares algorithm
Kernels
Least squares
Mean square error methods
minimum mean square error
Nearest-instance-centroid-estimation
nonlinear problems
Performance degradation
Prediction algorithms
quantization
Signal processing algorithms
Vector quantization
ISSN:1549-7747, 1558-3791
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Abstract The nearest-instance-centroid-estimation kernel least mean-square (NICE-KLMS) algorithm has been proposed to balance the time and space requirements in kernel adaptive filters. However, the minimum mean square error (MMSE) criterion used in NICE-KLMS leads to performance degradation in some nonlinear problems. In this brief, the NICE is developed under the least-squares errors in the kernel space, generating a novel NICE kernel recursive least squares (NICE-KRLS) algorithm for performance improvement of NICE-KLMS. The weight update form for the solution to the least-squares errors existing in NICE-KRLS is therefore obtained recursively. To obtain a sparsification network, the vector quantization is combined into NICE-KRLS for online applications. Simulations on chaotic time-series prediction validate the superiorities of the proposed NICE-KRLS and its sparsification version.
AbstractList The nearest-instance-centroid-estimation kernel least mean-square (NICE-KLMS) algorithm has been proposed to balance the time and space requirements in kernel adaptive filters. However, the minimum mean square error (MMSE) criterion used in NICE-KLMS leads to performance degradation in some nonlinear problems. In this brief, the NICE is developed under the least-squares errors in the kernel space, generating a novel NICE kernel recursive least squares (NICE-KRLS) algorithm for performance improvement of NICE-KLMS. The weight update form for the solution to the least-squares errors existing in NICE-KRLS is therefore obtained recursively. To obtain a sparsification network, the vector quantization is combined into NICE-KRLS for online applications. Simulations on chaotic time-series prediction validate the superiorities of the proposed NICE-KRLS and its sparsification version.
Author Zhang, Haonan
Wang, Shiyuan
Wang, Lin
Zhang, Tao
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10.1109/TNET.2012.2187923
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10.1109/TNNLS.2013.2258936
10.1109/TNN.2003.810597
10.1109/TSP.2004.830985
10.1109/TSP.2017.2752695
10.1109/TSP.2007.907881
10.1109/81.933330
10.1162/neco.1991.3.2.213
10.1109/TCSII.2017.2778302
10.1016/j.dsp.2015.09.015
10.1109/TCSII.2016.2603193
10.1155/2008/784292
10.1109/TCSII.2013.2281712
10.1109/TCSI.2018.2819655
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References ref13
ref12
ref15
ref14
ref10
ref2
ref17
ref16
ref8
ref7
ref9
ref4
mitchell (ref1) 1997
ref3
ref6
ref5
liu (ref11) 2009; 20
References_xml – ident: ref12
  doi: 10.1109/TNNLS.2011.2178446
– volume: 20
  start-page: 1950
  year: 2009
  ident: ref11
  article-title: An information theoretic approach of designing sparse kernel adaptive filters
  publication-title: IEEE Trans Neural Netw
  doi: 10.1109/TNET.2012.2187923
– ident: ref4
  doi: 10.1002/9780470608593
– ident: ref15
  doi: 10.1109/TNNLS.2013.2258936
– ident: ref9
  doi: 10.1109/TNN.2003.810597
– ident: ref8
  doi: 10.1109/TSP.2004.830985
– ident: ref17
  doi: 10.1109/TSP.2017.2752695
– ident: ref5
  doi: 10.1109/TSP.2007.907881
– year: 1997
  ident: ref1
  publication-title: Machine Learning
– ident: ref3
  doi: 10.1109/81.933330
– ident: ref10
  doi: 10.1162/neco.1991.3.2.213
– ident: ref13
  doi: 10.1109/TCSII.2017.2778302
– ident: ref14
  doi: 10.1016/j.dsp.2015.09.015
– ident: ref16
  doi: 10.1109/TCSII.2016.2603193
– ident: ref6
  doi: 10.1155/2008/784292
– ident: ref7
  doi: 10.1109/TCSII.2013.2281712
– ident: ref2
  doi: 10.1109/TCSI.2018.2819655
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SubjectTerms Adaptive filters
Algorithms
Centroids
Circuits and systems
Clustering algorithms
Computer simulation
Dictionaries
Kernel
kernel recursive least squares algorithm
Kernels
Least squares
Mean square error methods
minimum mean square error
Nearest-instance-centroid-estimation
nonlinear problems
Performance degradation
Prediction algorithms
quantization
Signal processing algorithms
Vector quantization
Title The Nearest-Instance-Centroid-Estimation Kernel Recursive Least Squares Algorithms
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