NP-Hardness and Inapproximability of Sparse PCA
Machine Learning
2015-02-23 v2 Computational Complexity
Data Structures and Algorithms
Combinatorics
Machine Learning
Abstract
We give a reduction from {\sc clique} to establish that sparse PCA is NP-hard. The reduction has a gap which we use to exclude an FPTAS for sparse PCA (unless P=NP). Under weaker complexity assumptions, we also exclude polynomial constant-factor approximation algorithms.
Cite
@article{arxiv.1502.05675,
title = {NP-Hardness and Inapproximability of Sparse PCA},
author = {Malik Magdon-Ismail},
journal= {arXiv preprint arXiv:1502.05675},
year = {2015}
}