  We use persistent homology along with the eigenfunctions of the Laplacian to
study similarity amongst triangulated 2-manifolds. Our method relies on
studying the lower-star filtration induced by the eigenfunctions of the
Laplacian. This gives us a shape descriptor that inherits the rich information
encoded in the eigenfunctions of the Laplacian. Moreover, the similarity
between these descriptors can be easily computed using tools that are readily
available in Topological Data Analysis. We provide experiments to illustrate
the effectiveness of the proposed method.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning