Sparse PCA with Oracle Property

@article{Gu2014SparsePW,
  title={Sparse PCA with Oracle Property},
  author={Quanquan Gu and Zhaoran Wang and Han Liu},
  journal={Advances in neural information processing systems},
  year={2014},
  volume={2014},
  pages={1529-1537}
}
In this paper, we study the estimation of the k-dimensional sparse principal subspace of covariance matrix Σ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori. To this end, we propose a family of estimators based on the semidefinite relaxation of sparse PCA with novel regularizations. In particular, under a weak assumption on the magnitude of the population… CONTINUE READING
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