Regression for sets of polynomial equations

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

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

We propose a method called ideal regression for approximating an arbitrary system of polynomial equations by a system of a particular type. Using techniques from approximate computational algebraic geometry, we show how we can solve ideal regression directly without resorting to numerical optimization. Ideal regression is useful whenever the solution to a learning problem can be described by a system of polynomial equations. As an example, we demonstrate how to formulate Stationary Subspace Analysis (SSA), a source separation problem, in terms of ideal regression, which also yields a consistent estimator for SSA. We then compare this estimator in simulations with previous optimization-based approaches for SSA.

Original languageEnglish
Pages (from-to)628-637
Number of pages10
JournalJournal of Machine Learning Research
Volume22
Publication statusPublished - 2012
Externally publishedYes

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., Müller, J. S., Blythe, D. A. J., Meinecke, F. C., & Muller, K. (2012). Regression for sets of polynomial equations. Journal of Machine Learning Research, 22, 628-637.