  We introduce an extension to local principal component analysis for learning
symmetric manifolds. In particular, we use a spectral method to approximate the
Lie algebra corresponding to the symmetry group of the underlying manifold. We
derive the sample complexity of our method for a variety of manifolds before
applying it to various data sets for improved density estimation.
