The isomap technique is I think one of the coolest developments in dimensionality reduction in recent times. I am not such a big fan of LLE, but then that is just a matter of personal taste. I find the idea of approximating distances between points using the geodesic distances to be ubercool. As for the applicability of the idea itself, Given the fact that data very frequently is represented in a much larger space than the one it lives in, mostly because the space that it lives in is a nonlinear manifold and the only way to represent the data in a linear space is to imbed it in a really large number of dimensions (e.g. faces) or that the raw data comes to you in high dimensional yet highly constrained form, this technique can give you dimensionality reduction while still preserving the pairwise distances between the points. Ofcourse this means that whatever meaning the individual dimensions had in the original data is lost.