Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 8, Issue 5, Pages 137-139Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/97.917695
Keywords
knot; least squares; model selection; smoothing; spline; wavelet decomposition
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A critical component of spline smoothing is the choice of knots, especially for curves with varying shapes and frequencies in its domain. We consider a two-stage knot selection scheme for adaptively fitting splines to data subject to noise. A potential set of knots is chosen based on information from certain wavelet decompositions with the intention of placing more points where the curve shows rapid changes. The final knot selection is then made based on statistical model selection ideas. We show that the proposed method is well suited for a variety of smoothing and noise filtering needs.
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