👉 Multivariate Principal Component Analysis (mPCMA) is a statistical technique that extends traditional PCA to handle multivariate data, where each observation is a vector in a high-dimensional space. Unlike standard PCA, which extracts principal components from univariate data, mPCMA simultaneously captures the relationships between multiple variables within a dataset, providing a more holistic and efficient dimensionality reduction. This method is particularly useful when dealing with complex datasets where variables are intercorrelated, as it can identify a smaller set of principal components that explain the maximum variance across all variables, thus simplifying data interpretation and visualization while retaining critical information. By considering the covariance structure of multiple variables, mPCMA offers a more nuanced and accurate representation of the underlying data structure compared to analyzing each variable independently.
