Arbeitspapier

Sparse non Gaussian component analysis by semidefinite programming

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Sparse non-Gaussian component analysis (SNGCA) is an unsupervised method of extracting a linear structure from a high dimensional data based on estimating a low-dimensional non-Gaussian data component. In this paper we discuss a new approach to direct estimation of the projector on the target space based on semidefinite programming which improves the method sensitivity to a broad variety of deviations from normality. We also discuss the procedures which allows to recover the structure when its effective dimension is unknown.

Sprache
Englisch

Erschienen in
Series: SFB 649 Discussion Paper ; No. 2011-080

Klassifikation
Wirtschaft
Semiparametric and Nonparametric Methods: General
Thema
dimension reduction
non-Gaussian components analysis
feature extraction

Ereignis
Geistige Schöpfung
(wer)
Diederichs, Elmar
Juditsky, Anatoli
Nemirovski, Arkadi
Spokoiny, Vladimir
Ereignis
Veröffentlichung
(wer)
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
(wo)
Berlin
(wann)
2011

Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
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Objekttyp

  • Arbeitspapier

Beteiligte

  • Diederichs, Elmar
  • Juditsky, Anatoli
  • Nemirovski, Arkadi
  • Spokoiny, Vladimir
  • Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk

Entstanden

  • 2011

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