"The Currier languages revisited" revisited

[Rafal]

I mean, suppose folio A and folio B are correlated under metric C (of whatever the proper lingo is). So what?

It means that they are similar on some dimension.

PCA (Principal Component Analysis) takes a lot of dimensions and reduces them to a smaller number of dimensions, let's say 2, although you can choose any number.

You lose some information by that and it is an important question how much you lose.

Imagine having two original dimensions for people - height and weight. You decide to reduce them to one dimension. What you get is one dimension telling if someone is "big" or "small". A man who is very tall will be "big" and a man who is very fat will be "big" too. But you lost some details.

In our Voynich analysis people took frequency of some letters bigrams. You have, let's say, 40 frequencies of most common bigrams for each page. They make 40-dimensional vector of original values. Such stuff is impossible to imagine visually but perfectly possible to work with it numerically. Then you reduce it to 2 dimensions.

These new dimensions are some abstraction and usually don't have any nice name in English language. You can just check them how they are "loaded" with your original dimensions which are frequency bigrams.