Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/13F9D7BC-B98C-40E2-9705-5E257B3D5EFE | ||
| Year | 2014 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
|
||
| Bibliographic Citation | M. Asteris, D. S. Papailiopoulos, and G. N. Karystinos, "The sparse principal component of a constant-rank matrix," IEEE Transactions on Information Theory,vol. 60, no. 4, pp. 2281 - 2290, Apr. 2014. doi: 10.1109/TIT.2014.2303975 https://doi.org/10.1109/TIT.2014.2303975 | ||
| Appears in Collections |
The computation of the sparse principal component of a matrix is equivalent to the identification of its principal submatrix with the largest maximum eigenvalue. Finding this optimal submatrix is what renders the problem NP-hard. In this paper, we prove that, if the matrix is positive semidefinite and its rank is constant, then its sparse principal component is polynomially computable. Our proof utilizes the auxiliary unit vector technique that has been recently developed to identify problems that are polynomially solvable. In addition, we use this technique to design an algorithm which, for any sparsity value, computes the sparse principal component with complexity O(ND+1), where N and D are the matrix size and rank, respectively. Our algorithm is fully parallelizable and memory efficient.
Copyright © DIAS 2013
