Complex independent process analysis

Zoltán Szabó, András Lorincz

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

We present a general framework for the search of hidden independent processes in the complex domain. The task is to estimate the hidden independent multidimensional complex-valued components observing only the mixture of the processes driven by them. In our model (i) the hidden independent processes can be multidimensional, they may be subject to (ii) moving averaging, or may evolve in an autoregressive manner, or (iii) they can be non-stationary. These assumptions are covered by integrated autoregressive moving average processes and thus our task is to solve their complex extensions. We show how to reduce the undercomplete version of complex integrated autoregressive moving average processes to real independent subspace analysis that we can solve. Simulations illustrate the working of the algorithm.

Original languageEnglish
Pages (from-to)177-190
Number of pages14
JournalActa Cybernetica
Volume19
Issue number1
DOIs
Publication statusPublished - 2009

ASJC Scopus subject areas

  • Software
  • Computer Science (miscellaneous)
  • Computer Vision and Pattern Recognition
  • Management Science and Operations Research
  • Information Systems and Management
  • Electrical and Electronic Engineering

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