| Full text | |
| Author(s): |
Vieira, Guilherme
;
Valle, Marcos Eduardo
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2020-01-01. |
| Abstract | |
This paper aims to provide a useful framework for extreme learning machines (ELMs) on Cayley-Dickson algebras. Cayley-Dickson algebras, which include complex numbers, quaternions, and octonions as particular instances, are hyper-complex algebras defined using a recursive procedure. Firstly, we review some basic concepts on Cayley-Dickson algebras and formulate Cayley-Dickson matrix product using real-valued linear algebra. Then, we propose the Cayley-Dickson ELMs and derive their learning using Cayley-Dickson least squares problem. Lastly, we compare the performance of real-valued and four-dimensional Cayley-Dickson ELM models, including quaternion-valued ELM, in an experiment on color image auto-encoding using the well-known CIFAR dataset. (AU) | |
| FAPESP's process: | 19/02278-2 - Mathematical Morphology and Morphological Neural Networks for Multivalued Data |
| Grantee: | Marcos Eduardo Ribeiro Do Valle Mesquita |
| Support Opportunities: | Regular Research Grants |