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Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space

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Figure

Figure 1: First three principal components (PC) extracted by kernel PCA (left) and components(C) extracted by kernel PLS (right)
Figure 2: Sinc function approximation. Dependence of the training and testing error of kernelPLS (solid line) and kernel PCR (dashed line) on the number of extracted components.Gaussian noise with standard deviation equal to 0.2 was added to the training d
Figure 4: Example of the estimated ordered eigenvalues (left) and t2 statistic (right) of the regres-sors created by the projection onto corresponding principal components
Table 1: Goodness of prediction in R2 terms. The values correspond to an average of 25 differentsimulations.Noise level is represented as the ratio between the standard deviation ofthe added Gaussian noise and the standard deviation of the generated response variablesy1, y2, y3, y4 (22).
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