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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.

Language
Englisch

Bibliographic citation
Series: SFB 649 Discussion Paper ; No. 2011-080

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

Event
Geistige Schöpfung
(who)
Diederichs, Elmar
Juditsky, Anatoli
Nemirovski, Arkadi
Spokoiny, Vladimir
Event
Veröffentlichung
(who)
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
(where)
Berlin
(when)
2011

Handle
Last update
10.03.2025, 11:41 AM CET

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Object type

  • Arbeitspapier

Associated

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

Time of origin

  • 2011

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