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AIPrincipal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance as possible in a dataset.
It transforms the original correlated variables into a new set of uncorrelated variables called principal components.
The process begins by centering the data (which is subtracting the mean), then computing the covariance matrix to capture the relationships between variables.
Eigenvectors and their corresponding eigenvalues are then calculated from the covariance matrix. The eigenvectors represent the directions/principal components of the highest variance, while the eigenvalues quantify the amount of variance in each direction.
By selecting the top “k” eigenvectors with the highest eigenvalues, PCA projects the data into a lower dimensional space, simplifying analysis and visualization while retaining the most important information.
C: deepia
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