Principal Component Analysis
**Principal Component Analysis**
What is Principal Component Analysis?
Principal Component Analysis (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. PCA is used for dimensionality reduction, feature extraction, and data visualization. It is commonly used in fields like image processing, bioinformatics, and finance to simplify data sets and identify underlying patterns.
What other technologies are related to Principal Component Analysis?
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