On compressibility and acceleration of orthogonal nmf for pomdp compression

Xin Li*, Kwok Wai CHEUNG, Jiming LIU

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review


State space compression is one of the recently proposed approaches for improving POMDP's tractability. Despite its initial success, it still carries two intrinsic limitations. First, not all POMDP problems can be compressed equally well. Also, the cost of computing the compressed space itself may become significant as the size of the problem is scaled up. In this paper, we address the two issues with respect to an orthogonal non-negative matrix factorization recently proposed for POMDP compression. In particular, we first propose an eigenvalue analysis to evaluate the compressibility of a POMDP and determine an effective range for the dimension reduction. Also, we incorporate the interior-point gradient acceleration into the orthogonal NMF and derive an accelerated version to minimize the compression overhead. The validity of the eigenvalue analysis has been evaluated empirically. Also, the proposed accelerated orthogonal NMF has been demonstrated to be effective in speeding up the policy computation for a set of robot navigation related problems.

Original languageEnglish
Title of host publicationAdvances in Machine Learning - First Asian Conference on Machine Learning, ACML 2009, Proceedings
Number of pages15
Publication statusPublished - 2009
Event1st Asian Conference on Machine Learning, ACML 2009 - Nanjing, China
Duration: 2 Nov 20094 Nov 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5828 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st Asian Conference on Machine Learning, ACML 2009

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)


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