A statistical technique that transforms a large set of variables into a smaller set of uncorrelated components that capture the maximum variance in the data. It's used for data reduction and to identify patterns in complex datasets.
Developed by Karl Pearson in 1901 and later formalized by Harold Hotelling in 1933. 'Principal' indicates the most important components (those explaining the most variance), while 'component' refers to the new variables created from combinations of original variables.
Imagine trying to describe a person's face with 1000 measurements—PCA finds the few key dimensions (like 'how long,' 'how wide') that capture most of what makes faces different! It's data compression for statistics.
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