Systems and methods for robust low-rank matrix approximation
US10229092B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 14, 2017 |
| Grant date | Mar 12, 2019 |
| Priority date | — |
| Expiry date | Aug 14, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2218/22
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods which provide robust low-rank matrix approximation using low-rank matrix factorization in the lp-norm space, where p<2 (e.g., 1≤p<2), providing a lp-PCA technique are described. For example, embodiments are configured to provide robust low-rank matrix approximation using low-rank matrix factorization in the least absolute deviation (l1-norm) space providing a l1-PCA technique. Embodiments minimize the lp-norm of the residual matrix in the subspace factorization of an observed data matrix, such as to minimize the l1-norm of the residual matrix where p=1. The alternating direction method of multipliers (ADMM) is applied according to embodiments to solve the subspace decomposition of the low-rank matrix factorization with respect to the observed data matrix. Iterations of the ADMM may comprise solving a l2-subspace decomposition and calculating the proximity operator of the l1-norm.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.