Pattern change discovery between high dimensional data sets
US11947622B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 7, 2020 |
| Grant date | Apr 2, 2024 |
| Priority date | — |
| Expiry date | Oct 27, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/213
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The general problem of pattern change discovery between high-dimensional data sets is addressed by considering the notion of the principal angles between the subspaces is introduced to measure the subspace difference between two high-dimensional data sets. Current methods either mainly focus on magnitude change detection of low-dimensional data sets or are under supervised frameworks. Principal angles bear a property to isolate subspace change from the magnitude change. To address the challenge of directly computing the principal angles, matrix factorization is used to serve as a statistical framework and develop the principle of the dominant subspace mapping to transfer the principal angle based detection to a matrix factorization problem. Matrix factorization can be naturally embedded into the likelihood ratio test based on the linear models. The method may be unsupervised and addresses the statistical significance of the pattern changes between high-dimensional data sets.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.