Is Manifold Learning for Toy Data Only?

Manifold learning algorithms aim to  recover the underlying low-dimensional parametrization of the data using  either local or global features. It is however widely recognized that the low dimensional parametrizations will typically distort the geometric properties of the original data, like distances and angles. These impredictible and algorithm dependent distortions make it unsafe to pipeline the output of a manifold learning algorithm into other data analysis algorithms, limiting the use of these techniques in engineering and the sciences.

Moreover, accurate manifold learning typically requires very large sample sizes, yet existing implementations are not scalable, which has led to the commonly held belief that manifold learning algorithms aren’t practical for real data.

This talk will show how both limitations can be overcome. I will present a statistically founded methodology to estimate and then cancel out the distortions introduced by any embedding algorithm, thus effectively preserving the distances in the original data. This method builds on the relationship between the Laplace-Beltrami operator and the Riemannian metric on a manifold; the relationship  can also be used to estimate vector fields on a manifold, the kernel width and intrinsic dimension, or to remove distortions induced by the embedding algorithms. On the computational side I will demonstrate that with careful use of sparse data structures manifold learning can scale to data sets in the millions.

Joint work with Dominique Perrault-Joncas, James McQueen, Jacob VanderPlas, Zhongyue Zhang, Yu-Chia Chen, Grace Telford.

Marina Meila, University of Washington