The development of semidefinite sparse component analysis

Sparse non-Gaussian component analysis is an unsupervised linear method of extracting any structure from high-dimensional distributed data based on estimating a low-dimensional non-Gaussian data component. In this paper we discuss a new approach with known a priori reduced dimension to direct estimation of the projector on the target space using semidefinite programming. The new approach avoids the estimation of the data covariance matrix and overcomes the traditional separation of element estimation of the target space and target space reconstruction. This allows to reduced the sampling size while improving the sensitivity to a broad variety of deviations from normality. Moreover the complexity of the new approach is limited to $O(d \log d)$.