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Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated variables into uncorrelated principal components, capturing most of the data variance. It involves standardization, covariance matrix computation, eigen decomposition, and projection to reduce dimensions while retaining data structure. PCA has applications in image compression, noise filtering, and data visualization, but it has limitations such as assuming linearity and being sensitive to scaling.

Author

harsharaveen123

Language

EN