How Should PCA Handle Components Dominated by a Single Variable?
After performing Principal Component Analysis (PCA), if a particular principal component is predominantly influenced by only one variable, with minimal contributions from the others, it may be advisable to consider excluding this component from further analysis. The rationale is to prevent disproportionate reliance on a single variable, which could skew the results and undermine the representativeness and robustness of the PCA.
When deciding whether to exclude such a component, it is also essential to evaluate additional factors, such as the interpretability of the data structure and the cumulative proportion of variance explained by the retained components. If excluding the component leads to a substantial reduction in explanatory power or if the remaining components fail to capture the major patterns in the data, then retaining the component may be more appropriate.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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