Vector Space, overlapping genres, and the world beyond keyword search

I wanted to see how well the vector space model of documents I've been using for PCA works at classifying individual books. [Note at the outset: this post swings back from the technical stuff about halfway through, if you're sick of the charts.] While at the genre level the separation looks pretty nice, some of my earlier experiments with PCA, as well as some of what I read in the Stanford Literature Lab's Pamphlet One, made me suspect individual books would be sloppier. There are a couple different ways to ask this question. One is to just drop the books as individual points on top of the separated genres, so we can see how they fit into the established space. By the first two principal components, for example, we can make all the books  in LCC subclasses "BF" (psychology) blue, and use red for "QE" (Geology), overlaying them on a chart of the first two principal components like I've been using for the last two posts:

That's a little worse than I was hoping. Generally the books stay close to their term, but there is a lot of variation, and even a little bit of overlap. Can we do better? And what would that mean?