How to Perform Principal Component Analysis When There Is No Dependent Variable?
Principal Component Analysis (PCA) is an unsupervised statistical method designed to identify the principal modes or directions of maximum variance within a dataset. Consequently, PCA does not involve a dependent variable. Its primary purpose is dimensionality reduction, aiming to retain the greatest possible proportion of the original variance within the data.
Specifically, PCA transforms high-dimensional data into a new coordinate system defined by principal components. These principal components sequentially capture the maximum variance: the first principal component captures the largest variance, the second principal component, orthogonal to the first, captures the next largest variance, and this process continues similarly for subsequent components.
PCA operates exclusively on input features or variables, independent of any dependent or response variables.
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