Sparse Principal Component Analysis with Missing Observations

@article{Lounici2013SparsePC,
  title={Sparse Principal Component Analysis with Missing Observations},
  author={Karim Lounici},
  journal={arXiv: Statistics Theory},
  year={2013},
  pages={327-356}
}
  • Karim Lounici
  • Published 31 May 2012
  • Mathematics, Computer Science
  • arXiv: Statistics Theory
In this paper, we study the problem of sparse Principal Component Analysis (PCA) in the high dimensional setting with missing observations. Our goal is to estimate the first principal component when we only have access to partial observations. Existing estimation techniques are usually derived for fully observed data sets and require a prior knowledge of the sparsity of the first principal component in order to achieve good statistical guarantees. Our contributions is essentially theoretical in… 
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