Factor analysis is another technique that we can use to reduce dimensionality. However, factor analysis makes assumptions and PCA does not. The basic assumption is that there are implicit features responsible for the features of the dataset.
This recipe will boil down to the explicit features from our samples in an attempt to understand the independent variables as much as the dependent variables.