Modeling complex systems by generalized factor analysis

G. Bottegal, G Picci

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)


We propose a new modeling paradigm for large dimensional aggregates of stochastic systems by Generalized Factor Analysis (GFA) models. These models describe the data as the sum of a flocking plus an uncorrelated idiosyncratic component. The flocking component describes a sort of collective orderly motion which admits a much simpler mathematical description than the whole ensemble while the idiosyncratic component describes weakly correlated noise. We first discuss static GFA representations and characterize in a rigorous way the properties of the two components. The extraction of the dynamic flocking component is discussed for time-stationary linear systems and for a simple classes of separable random fields.
Original languageEnglish
Pages (from-to)759-774
Number of pages16
JournalIEEE Transactions on Automatic Control
Issue number3
Publication statusPublished - 2015
Externally publishedYes


  • Analytical models
  • Biological system modeling
  • Collective behavior
  • Covariance matrices
  • Mathematical model
  • Noise
  • Random variables
  • Vectors
  • complex systems
  • flocking
  • generalized factor analysis
  • multi-agent systems
  • stochastic systems


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