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The wavelet basis with minimum reconstruction error is chosen.
The LRC is developed based on the minimum reconstruction error.
LRC uses the least-square method to decide the class label with the minimum reconstruction error.
The KLRC is also developed based on the minimum reconstruction error.
If the signal has more than K non-zero transform coefficients, the recovery process guarantees minimum reconstruction error.
Ideally, the KPCA model trained from the class which x also belongs to will always give the minimum reconstruction error.
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Specifically we reformulate the summarization problem into a dictionary learning problem by selecting bases which can be sparsely combined to represent the original image and achieve a minimum global reconstruction error, such as MSE (Mean Square Error).
One of the main results obtained is the proof of existence of the optimal vibration model of the structure,which renders minimum to the reconstruction error.
Most such algorithms provide bounds for the reconstruction error based on the assumption that the corrupting noise is bounded, Gaussian, or, at a minimum, has finite variance.
In our study, we assess the data reconstruction error using the mean squared error normalized by the data size and by the difference between the maximum and minimum value in the data.
At last, the reconstruction error is calculated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com