Is feature scaling useful for PCA?

Principal Component Analysics (PCA) is also a good example of when feature scaling is important since we are interested in the components that maximize the variance and therefore we need to ensure that we are comparing apples to apples.

Is PCA sensitive to scaling?

PCA is sensitive to the relative scaling of the original variables. Principal components analysis is a procedure for identifying a smaller number of uncorrelated variables, called “principal components”, from a large set of data. Principal components analysis is commonly used as one step in a series of analyses.

How might centering the data affect the first principal component?

Centering is an important pre-processing step because it ensures that the resulting components are only looking at the variance within the dataset, and not capturing the overall mean of the dataset as an important variable (dimension).

Do you need to center data before PCA?

Without mean-centering, the first principal component found by PCA might correspond with the mean of the data instead of the direction of maximum variance. Once the data has been centered (and possibly scaled, depending on the units of the variables) the covariance matrix of the data needs to be calculated.

Does mean centering change significance?

Centering should not change the significance of any interaction term but it may change for the component variables of the interaction. This means that the variable’s significance is different evaluated at the mean and zero.

When should you center a variable?

In regression, it is often recommended to center the variables so that the predictors have mean 0. This makes it easier to interpret the intercept term as the expected value of Yi when the predictor values are set to their means.

Should you scale data before PCA?

Yes, it is necessary to normalize data before performing PCA. The PCA calculates a new projection of your data set. If you normalize your data, all variables have the same standard deviation, thus all variables have the same weight and your PCA calculates relevant axis.