Hypercomplex deep learning methods, architectures, and apparatus for multimodal small, medium, and large-scale data representation, analysis, and applications
US11645835B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 30, 2018 |
| Grant date | May 9, 2023 |
| Priority date | — |
| Expiry date | Nov 29, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/194
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and system for creating hypercomplex representations of data includes, in one exemplary embodiment, at least one set of training data with associated labels or desired response values, transforming the data and labels into hypercomplex values, methods for defining hypercomplex graphs of functions, training algorithms to minimize the cost of an error function over the parameters in the graph, and methods for reading hierarchical data representations from the resulting graph. Another exemplary embodiment learns hierarchical representations from unlabeled data. The method and system, in another exemplary embodiment, may be employed for biometric identity verification by combining multimodal data collected using many sensors, including, data, for example, such as anatomical characteristics, behavioral characteristics, demographic indicators, artificial characteristics. In other exemplary embodiments, the system and method may learn hypercomplex function approximations in one environment and transfer the learning to other target environments. Other exemplary applications of the hypercomplex deep learning framework include: image segmentation; image quality evaluation; image ste…
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