Principal Component transformation is a method that stands for reduction of the dimension, that means try to reshape your space in a way that a great part of the total information can be explained with the first few axes. and those axes are actually a linear combination of the initial axes ( variables) , so yes PCA can catch the interaction between variables but only a little , cause to express the interaction between 2 ,3 4 ... variables there a lot of advanced ( aggregation for example) things to do then a simple linear combination between the 2,3 4 ... if you get my idea.
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