4 ms·

I agree. I'm surprised with the number of situations where people use PCA when ICA really would be more appropriate. I suppose that's the difference between k

• nextos 10y ago
  The late David McKay said this about PCA: "Principal Component Analysis is a dimensionally invalid method that gives people a delusion that they are doing something useful with their data. If you change the units that one of the variables is measured in, it will change all the "principal components"! It's for that reason that I made no mention of PCA in my book. I am not a slavish conformist, regurgitating whatever other people think should be taught. I think before I teach." —https://www.amazon.com/review/R16RJ2PT63DZ3Q https://www.amazon.com/review/R16RJ2PT63DZ3Q
  • Scea91 10y ago
    I wouldn't read a book from an author who reasons and writes like that. The issue he is having with PCA can be solved by normalizing the data.
    • conjectures 10y ago
      McKay did foundational research in machine learning; was a chief scientific advisor to the UK government's department for climate change; created eye tracking user interface software for paralysed people; and wrote an excellent text book on information theory. I.e. a proper Olympian. So when you read a statement like that, you should take it seriously and possibly realign your world-view.
    • Xcelerate 10y ago
      I'm sure David MacKay is well aware of the need for normalization — his point is that people use PCA without understanding what it means and how to perform it properly. Memorizing a technique without having any idea of why the technique works often leads to meaningless (or worse, incorrect) interpretations of data.