Dynamic programming techniques are useful in smoothing and differentiating noisy data signals according to an optimization criterion and the results are generally quite robust to noise spectra different from that assumed in the construction of the filter. If the noise properties are sufficiently different, however, the generalized cross-validation function used in the optimization can exhibit either multiple minima or no minima other than that corresponding to an insignificant amount of smoothing; in these cases, the smoothing parameter desired by the user typically does not lie at the global minimum of the generalized cross-validation function, but at some other point on the curve which can be identified heuristically. I present two cases to demonstrate this phenomenon and describe what measures one can take to ensure that the desired smoothing parameter is obtained.
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November 1994
Technical Briefs
Considerations in Applying Dynamic Programming Filters to the Smoothing of Noisy Data
Antony J. Hodgson
Antony J. Hodgson
Harvard University—Massachusetts Institute of Technology, Division of Health Sciences and Technology, 3-146, 77 Massachusetts Ave., Cambridge, MA 02139
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Antony J. Hodgson
Harvard University—Massachusetts Institute of Technology, Division of Health Sciences and Technology, 3-146, 77 Massachusetts Ave., Cambridge, MA 02139
J Biomech Eng. Nov 1994, 116(4): 528-531 (4 pages)
Published Online: November 1, 1994
Article history
Received:
September 21, 1993
Revised:
June 24, 1994
Online:
March 17, 2008
Citation
Hodgson, A. J. (November 1, 1994). "Considerations in Applying Dynamic Programming Filters to the Smoothing of Noisy Data." ASME. J Biomech Eng. November 1994; 116(4): 528–531. https://doi.org/10.1115/1.2895805
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