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What is PCA: Dimensionality Reduction Explained about?
This document discusses principal component analysis (PCA), a technique for dimensionality reduction. PCA transforms a set of variables into a smaller set of variables called principal components, which contain most of the information in the original variables. The principal components are linear combinations of the original variables and are orthogonal to each other. PCA reduces the dimensionality of data while preserving as much information as possible.
- Author
- Prince Rathore
- Language
- EN