Mark Tomczak @mtomczak@qoto.org

Hey lazymastodon, I have a linear algebra question.

So I've been thinking a bit about principle component analysis as of late. The way to find the vector of most variance in a multidimensional dataset is to put every datapoint in a column matrix, multiply that matrix by its transpose, and find the eigenvectors of the resulting square matrix.

Here's my question: I don't have a good intuition for what "multiply the matrix by its transpose" is doing. That compares every point to every other point by multiplying the same-dimension components together and summing the result across dimensions, but like... Why does that result in an interesting matrix instead of a pile of noise?

This represents a not-insignificant part of my career.

The image that appears in my brain whenever someone says anything like "C is a fine language if you just use it right."