Principal Component Analysis

Principal Component Analysis (PCA) is a useful statistical technique for identifying patterns in large datasets, and expressing the data in such a way as to highlight their similarities and differences. PCA reduces the number of dimensions into new principal components.

The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. Each new principal component will be a sum of one or more of the original observations.

An Introduction to PCA

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