A Method for Filtering Noise Data by Blending Local Least Squares Fitting Curves

In field of numerical analysis, fitting points in 2D plane with a smooth curve is a widely investigated problem. In this paper, we propose a novel fitting method, which has ability of creating smooth curve approximating the points and filtering noises in the data. Our method is constructed based on...

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Bibliographic Details
Published in2009 Second International Workshop on Computer Science and Engineering Vol. 1; pp. 538 - 542
Main Authors Chun-Ling Fan, Ming-Yong Pang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2009
Subjects
Computer science
Curve fitting
Data engineering
Educational technology
Filtering
Interpolation
Least squares approximation
Least squares methods
local least square fitting
local point set
Noise measurement
Polynomials
weighted blending
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Summary:In field of numerical analysis, fitting points in 2D plane with a smooth curve is a widely investigated problem. In this paper, we propose a novel fitting method, which has ability of creating smooth curve approximating the points and filtering noises in the data. Our method is constructed based on the idea of blending local least squares fitting curves with radical weight function. The method first generates a polynomial approximation for each point based on least squares method. Then, these polynomial curves are locally blended with appropriate weights. Finally, a smooth curve is generated, which approximates the 2D data as defined by an error metric based on least-squares technique. Experimental results show that our method has a stable performance and can be used to process all kinds of data in different resolutions.
ISBN:9781424452859
9780769538815
0769538819
1424452856
DOI:10.1109/WCSE.2009.727