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Principal Component Analysis for Dimension Reduction

The standard descriptive technique for projecting high-dimensional data onto a low-dimensional subspace that best explains its spread, understood as minimising the sum of squared distances from points to a plane and then projecting orthogonally onto it. Includes the proportion of variation explained as a quality measure.

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Principal Component Analysis for Dimensionality Reduction in Machine Learning

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a high-dimensional data set into a smaller set of uncorrelated variables, called principal components, which…