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If you change the units that one of the variables is measured in, it will change all the "principal components"! So what ? The same is true of most machine le
by tgflynn 14y ago
If you change the units that one of the variables is measured in, it will change all the "principal components"!
So what ? The same is true of most machine learning methods including neural networks and SVM's, you just have to use the same units consistently.
- mturmon 14y agoQuite true. And, sometimes things are already in comparable units. For example, grayscale image intensities. I don't see the problem with PCA if it is used sensitively to identify the approximate dimensionality of "pancakes" embedded in a larger space.
- mbq 14y agoOr use a machine learning method that is fully invariant to all monotonic transformations, for instance random forest.
- wookietrader 14y agoWrong. Neural networks are affinity invariant. You can rotate, skew, translate the input data however you want, the optimum stays the same. Same for SVMs.
- tgflynn 14y agoBut the weights will be different, which it seems to me is comparable to saying that the principal components change.