Jan 18, 2020 in Machine Learning
Q:

Why rotation is required in PCA? What will happen if you don’t rotate the components?

1 Answer

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Jan 18, 2020

Rotation is a significant step in PCA as it maximizes the separation within the variance obtained by components. Due to this, the interpretation of components becomes easier.

The motive behind doing PCA is to choose fewer components that can explain the greatest variance in a dataset. When rotation is performed, the original coordinates of the points get changed. However, there is no change in the relative position of the components.

If the components are not rotated, then we need more extended components to describe the variance.

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