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What is Linear Dimensionality Reduction Overview about?

Linear dimensionality reduction transforms data into a lower dimensional space through a linear projection. It aims to preserve as much relevant information as possible while removing noise or redundant features. Principal component analysis (PCA) is an unsupervised linear dimensionality reduction technique that projects data onto new orthogonal axes oriented along the directions of maximum variance. It works by computing the eigenvectors of the covariance matrix, which correspond to the principal component

Author

shre

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EN