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In the standard kernel partial least squares (KPLS), the mapped data in the feature space need to be centralized before extraction of new score vectors. However, each vector of the centralized variables is often uniformly distributed, and some original features that can reflect the contribution of each variable to fault diagnosis might be lost. As a result, it might lead to misleading interpretations...
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