Multilevel Manifold Learning with Application to Spectral Clustering

摘要

In the past decade, a number of nonlinear dimensionality reduction methods using an affinity graph have been developed for manifold learning. This paper explores a multilevel framework with the goal of reducing the cost of unsuper-vised manifold learning and preserving the embedding quality at the same time. An application to spectral clustering is also presented. Experimental results indicate that our multilevel approach is an appealing alternative to standard techniques.