Describe the steps of PCA

Describe the steps of PCA



1) The first step of PCA is to subtract the mean of each explanatory variable from each observation:

2) Next, we must calculate the principal components of the data. Recall that the principal components are the eigenvectors of the data's covariance matrix ordered by their eigenvalues. The principal components can be found using two different techniques.

3) Next, we will project the data onto the principal components. The first eigenvector has the greatest eigenvalue and is the first principal component. We will build a transformation matrix in which each column of the matrix is the eigenvector for a principal component.

4) Finally, we will find the dot product of the data matrix and transformation matrix.

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