Maximum Variance Correction with Application to A* Search

Wenlin Chen, Kilian Weinberger, Yixin Chen
Proceedings of the 30th International Conference on Machine Learning, PMLR 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v28-chen13c, title = {Maximum Variance Correction with Application to A* Search}, author = {Chen, Wenlin and Weinberger, Kilian and Chen, Yixin}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {302--310}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {1}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/chen13c.pdf}, url = {https://proceedings.mlr.press/v28/chen13c.html}, 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.} }
Endnote
%0 Conference Paper %T Maximum Variance Correction with Application to A* Search %A Wenlin Chen %A Kilian Weinberger %A Yixin Chen %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-chen13c %I PMLR %P 302--310 %U https://proceedings.mlr.press/v28/chen13c.html %V 28 %N 1 %X 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.
RIS
TY - CPAPER TI - Maximum Variance Correction with Application to A* Search AU - Wenlin Chen AU - Kilian Weinberger AU - Yixin Chen BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/02/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-chen13c PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 1 SP - 302 EP - 310 L1 - http://proceedings.mlr.press/v28/chen13c.pdf UR - https://proceedings.mlr.press/v28/chen13c.html AB - 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. ER -
APA
Chen, W., Weinberger, K. & Chen, Y.. (2013). Maximum Variance Correction with Application to A* Search. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(1):302-310 Available from https://proceedings.mlr.press/v28/chen13c.html.

Related Material