Principal Component Analysis (PCA) is the focusing on only the ‘principal components’ among the many dimensions of a data set, such that one is reducing the dimensions of that data set by ignoring the ‘non-principal’ parts. PCA though, with eigen values & vectors, take on a slightly deeper approach. Typically, data sets that are handled under PCA are in a matrix format and the principal components, that are sought from a matrix would be a single vector column (or row) that is ...
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