Laboratory of Structural Methods of Data Analysis in Predictive
Modeling Moscow Institute of Physics and Technology
Sparse Non Gaussian Component Analysis by Semidefinite Programming
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.

Авторы: Nemirovski Arkadi , Spokoiny Vladimir , Diederichs, E, Juditsky

Дата: 16 ноября 2014

Статус: опубликована

Журнал: J. Machine Learning

Том: 91

Страницы: 211-238

Год: 2013

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