Difference between PCA (Principal Component Analysis) and Feature Selection

Question

What is the difference between Principal Component Analysis (PCA) and Feature Selection in Machine Learning? Is PCA a means of feature selection?

Answer 1

Just to add to the very good answers above. The difference is that PCA will try to reduce dimensionality by exploring how one feature of the data is expressed in terms of the other features(linear dependecy). Feature selection instead, takes the target into consideration. It will rank your input variables in terms of how useful they are to predict the target value. This is true for univariate feature selection. Multi variate feature selection can also do something that can be considered a form of PCA, in the sense that it will discard some of the features in the input. But don't take this analogy too far.