Principal components: A descent algorithm

Rebeca Salas-Boni, Esteban G. Tabak

Research output: Contribution to journalArticlepeer-review


A descent procedure is proposed for the search of low-dimensional subspaces of a high-dimensional space that satisfy an optimality criterion. Specifically, the procedure is applied to finding the subspace spanned by the first m singular components of an n-dimensional dataset. The procedure minimizes the associated cost function through a series of orthogonal transformations, each represented economically as the exponential of a skew-symmetric matrix drawn from a low-dimensional space.

Original languageEnglish (US)
Pages (from-to)162-175
Number of pages14
JournalJournal of Computational Physics
StatePublished - Jun 15 2014


  • Principal component analysis

ASJC Scopus subject areas

  • Numerical Analysis
  • Modeling and Simulation
  • Physics and Astronomy (miscellaneous)
  • General Physics and Astronomy
  • Computer Science Applications
  • Computational Mathematics
  • Applied Mathematics


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