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What is Understanding Principal Component Analysis about?

PCA can be used to reduce the dimensionality of high-dimensional data when the number of data points N is less than the original dimensionality D. It does this by computing the eigenvectors of the NxN covariance matrix XTX/N instead of the computationally infeasible DxD covariance matrix. This yields the same eigenvalues but the eigenvectors are XTwi and must be normalized. PCA transforms the data to a lower dimensional space that retains the most variance.

Author

AdityaShekhar

Language

EN