A fast fixed-point algorithm for complexity pursuit

Zhenwei Shi, Huanwen Tang, Yiyuan Tang

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Complexity pursuit is a recently developed algorithm using the gradient descent for separating interesting components from time series. It is an extension of projection pursuit to time series data and the method is closely related to blind separation of time-dependent source signals and independent component analysis (ICA). In this paper, a fixed-point algorithm for complexity pursuit is introduced. The fixed-point algorithm inherits the advantages of the well-known FastICA algorithm in ICA, which is very simple, converges fast, and does not need choose any learning step sizes.

Original languageEnglish (US)
Pages (from-to)529-536
Number of pages8
JournalNeurocomputing
Volume64
Issue number1-4 SPEC. ISS.
DOIs
StatePublished - Mar 2005
Externally publishedYes

Keywords

  • Blind source separation
  • Complexity pursuit
  • Independent component analysis
  • Projection pursuit
  • Time series

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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