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If the data is contaminated – by error, selectivity or bias – so too will be the patterns learned by the software.
The proposed algorithm is also effective even if data is contaminated by non-monotonic noisy samples.
In practice, the data is contaminated by noise, which we represent by the following data model (5).
The state estimation method proposed for use with such incomplete observation data (with information loss or distortion) is a theoretically based improvement of the wide sense digital filter, for situations in which the data is contaminated by an external noise of arbitrary distribution type.
The capabilities of the proposed methodology are illustrated in problems from nonlinear solid mechanics with special attention to cases where the data is contaminated with random noise and the scale of variability of the unknown field is smaller than the scale of the grid where observations are collected.
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When the data were contaminated (level of contamination > 0%), the relative biases were all negative, indicating that the point estimates were biased towards null.
Large-scale, structural health monitoring remains a challenge especially when I/O measurement data are contaminated by high-level noise.
This inevitably brings the accuracy and the reliability issues, especially when the data are contaminated by noise.
The interferograms, calculated using quick but less accurate orbit data, are contaminated by long-wavelength noise, preventing us from measuring the correct ground displacement.
It is also shown that this method has considerable noise-resisting ability in the case where the measured response function data are contaminated by certain level measurement noise.
M-estimation performs well when the problems with outliers are in the Y-direction (response direction) but might yield improper results if the data are contaminated in the X-space (that is, in the covariate space).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com