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For the systems with nonlinear characteristics, some modified algorithms based on kernel partial least squares (KPLS) have also been designed very recently.
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New approaches are proposed for nonlinear process monitoring and fault diagnosis based on kernel principal component analysis (KPCA) and kernel partial least analysis (KPLS) models at different scales, which are called multiscale KPCA (MSKPCA) and multiscale KPLS MSKPLSS).
Additionally, PDF estimators based on kernel functions are also developed.
In this paper, a multivariate data modeling approach is proposed based on modified kernel partial least squares (MKPLS) with the signal filtering method.
However, some other feature extraction methods have put forward new ways which are based on kernels.
Based on reproducing kernel theory, reproducing kernel functions with polynomial form will be constructed in the reproducing kernel spaces spanned by the Chebyshev basis polynomials.
To this end, we first define the local kernel alignment based on centralized kernel alignment.
A kernel optimization method based on fusion kernel for high-resolution range profile (HRRP) is proposed in this paper.
Based on, the partial Euclidean distance (25).
Tests of significance were based on the partial likelihood method.
Effect-sizes have been determined based on the partial η².
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