The problem of PCA is that it is very different of the metric you put on your feature space. Divide/multiply per three the coordinate of one feature can move fr

The problem of PCA is that it is very different of the metric you put on your feature space. Divide/multiply per three the coordinate of one feature can move from one important axis to a small one. In most case where PCA is used for ML algorithm, it is a very important thing to take into account since you can lose a feature which is quite discriminating but that you'll squeeze in a discarded axis if its coordinate is too small. There are obviously methods to avoid this like whitening the input data but it doesn't cut it completely.