Home Page

Papers

Submissions

News

Scope

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Login



RSS Feed

Maximum Variance Correction with Application to A* Search

Wenlin Chen, Kilian Weinberger, Yixin Chen
;
JMLR W&CP 28 (1) : 302–310, 2013

Abstract

In this paper we introduce Maximum Variance Correction (MVC), which finds large-scale 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 (Rayner et al. 2011). We demonstrate that MVC embeddings lead to un-matched 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.

Related Material