Maximum Variance Correction with Application to A* Search

In this paper we introduce Maximum Variance Correction (MVC), which finds largescale feasible solutions to Maximum Variance Unfolding (MVU) by post-processing embeddings from any manifold learning algorithm. It increases the scale of MVU embeddings by several orders of magnitude and is naturally parallel. This unprecedented scalability opens up new avenues of applications for manifold learning, in particular the use of MVU embeddings as effective heuristics to speed-up A∗ search. We demonstrate unmatched reductions in search time across several non-trivial A∗ benchmark search problems and bridge the gap between the manifold learning literature and one of its most promising high impact applications.