What is intrinsic dimensionality estimation and why is it important?

It estimates the true number of variables needed to represent data.

Intrinsic dimensionality estimation techniques attempt to find the minimum number of parameters needed to describe the underlying manifold. Methods include nearest-neighbor statistics and maximum likelihood estimators. This helps choose appropriate dimensionality reduction techniques and prevents under- or over-compression.