The correct answer is D. Principal Component Analysis (PCA).
PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to (i.e., uncorrelated with) the preceding components.
PCA is often used for dimensionality reduction, as it can be used to represent the original data in a lower-dimensional space while still preserving most of the information. This can be useful for visualization, data analysis, and machine learning.
Data imputation is the process of filling in missing values in a dataset. Data normalization is the process of rescaling data so that it has a mean of 0 and a standard deviation of 1. Data aggregation is the process of combining data from multiple sources into a single dataset.
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