A model is a collection or composition of information represented by a data set. Usually, in the process of machine learning, too many features will increase the complexity of the model. The ideal state we desire is to use the fewest features to represent the most information in the data set. What kind of information (Linearly relevant or linearly irrelevant information) does the PCA try to remove? Why?