these higher dimensional planes are called hyper planesand they are defined by things called eigenvectors you take as many planes as you want dimensions in the endproject that data onto those hyperplanesand those become the new axes in your lower dimensional data spaceyou knowunless you're familiar with higher dimensional math and you've thought about it beforeit' going to be hard to wrap your head aroundbutat the end of the dayit means we're choosing planes in higher dimensional space that still preserve the most variance in our dataand project the data onto those higher dimensional planes that we then bring into lower dimensional spaceokayyou don' really have to understand all the math to use itthe important point is that it' very principled way of reducing dataset down to lower dimensional space while still preserving the variance within it we talked about image compression as one application of this so you knowif want to reduce the dimensionality in an imagei could use pca to boil it down to its essence