Information-theoretically optimal sparse PCA

@article{Deshpande2014InformationtheoreticallyOS,
  title={Information-theoretically optimal sparse PCA},
  author={Yash Deshpande and Andrea Montanari},
  journal={2014 IEEE International Symposium on Information Theory},
  year={2014},
  pages={2197-2201}
}
Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a low-rank representation of a data matrix with additional sparsity constraints on the obtained representation. We consider two probabilistic formulations of sparse PCA: a spiked Wigner and spiked Wishart (or spiked covariance) model. We analyze an Approximate Message Passing (AMP) algorithm to estimate the underlying signal and show, in the high dimensional limit, that the AMP estimates are… CONTINUE READING
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