Algebraic geometric comparison of probability distributions

Franz J. Király, Paul Von Bünau, Frank C. Meinecke, Duncan A J Blythe, Klaus Muller

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

We propose a novel algebraic algorithmic framework for dealing with probability distributions represented by their cumulants such as the mean and covariance matrix. As an example, we consider the unsupervised learning problem of finding the subspace on which several probability distributions agree. Instead of minimizing an objective function involving the estimated cumulants, we show that by treating the cumulants as elements of the polynomial ring we can directly solve the problem, at a lower computational cost and with higher accuracy. Moreover, the algebraic viewpoint on probability distributions allows us to invoke the theory of algebraic geometry, which we demonstrate in a compact proof for an identifiability criterion.

Original languageEnglish
Pages (from-to)855-903
Number of pages49
JournalJournal of Machine Learning Research
Volume13
Publication statusPublished - 2012 Mar 1

Keywords

  • Approximate algebra
  • Computational algebraic geometry
  • Unsupervised Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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  • Cite this

    Király, F. J., Von Bünau, P., Meinecke, F. C., Blythe, D. A. J., & Muller, K. (2012). Algebraic geometric comparison of probability distributions. Journal of Machine Learning Research, 13, 855-903.