  We examine the structure and dimensionality of the Jones polynomial using
manifold learning techniques. Our data set consists of more than 10 million
knots up to 17 crossings and two other special families up to 2001 crossings.
We introduce and describe a method for using filtrations to analyze infinite
data sets where representative sampling is impossible or impractical, an
essential requirement for working with knots and the data from knot invariants.
In particular, this method provides a new approach for analyzing knot
invariants using Principal Component Analysis. Using this approach on the Jones
polynomial data we find that it can be viewed as an approximately 3 dimensional
manifold, that this description is surprisingly stable with respect to the
filtration by the crossing number, and that the results suggest further
structures to be examined and understood.
