A Class of Weighted Quantized Kernel Recursive Least Squares Algorithms

In this brief, a class of weighted quantized kernel recursive least squares (WQKRLS) algorithms is proposed to efficiently improve the performance of online applications. In the proposed WQKRLS, an online vector quantization with weighted outputs is incorporated into quantized kernel recursive least...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. II, Express briefs Vol. 64; no. 6; pp. 730 - 734
Main Authors: Wang, Shiyuan, Wang, Wanli, Duan, Shukai
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive dictionary
Algorithms
Circuits and systems
Computational efficiency
Computer simulation
Dictionaries
Kernel
Least squares method
Measurement
Performance enhancement
quantized kernel recursive least squares (QKRLS)
Smoothing methods
sparsification
Steepest descent method
Vector quantization
ISSN:1549-7747, 1558-3791
Online Access:Get full text
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Summary:In this brief, a class of weighted quantized kernel recursive least squares (WQKRLS) algorithms is proposed to efficiently improve the performance of online applications. In the proposed WQKRLS, an online vector quantization with weighted outputs is incorporated into quantized kernel recursive least squares. The resulting desired outputs are smoothed by exponential weights. In addition, the members of the dictionary are updated by the steepest descent method for further performance improvement. Simulations illustrate the superior performance of the proposed WQKRLS.
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ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2016.2603193