Sparse PCA via Covariance Thresholding
Abstract
In sparse principal component analysis we are given noisy observations of a low-rank matrix of dimension $n\times p$ and seek to reconstruct it under additional sparsity assumptions. In particular, we assume here each of the principal components $v_1,\dots,v_r$ has at most $s_0$ non-zero entries. We are particularly interested in the high dimensional regime wherein $p$ is comparable to, or even much larger than $n$.
Cite
Text
Deshpande and Montanari. "Sparse PCA via Covariance Thresholding." Journal of Machine Learning Research, 2016.Markdown
[Deshpande and Montanari. "Sparse PCA via Covariance Thresholding." Journal of Machine Learning Research, 2016.](https://mlanthology.org/jmlr/2016/deshpande2016jmlr-sparse/)BibTeX
@article{deshpande2016jmlr-sparse,
title = {{Sparse PCA via Covariance Thresholding}},
author = {Deshpande, Yash and Montanari, Andrea},
journal = {Journal of Machine Learning Research},
year = {2016},
pages = {1-41},
volume = {17},
url = {https://mlanthology.org/jmlr/2016/deshpande2016jmlr-sparse/}
}