Abstract
This work proposes a scalable gamma non-negative matrix network (SGNMN), which uses a Poisson randomized Gamma factor analysis to obtain the neurons of the first layer of a network. These neurons obey Gamma distribution whose shape parameter infers the neurons of the next layer of the network and their related weights. Upsampling the connection weights follows a Dirichlet distribution. Downsampling hidden units obey Gamma distribution. This work performs up-down sampling on each layer to learn the parameters of SGNMN. Experimental results indicate that the width and depth of SGNMN are closely related, and a reasonable network structure for accurately detecting brain fatigue through functional near-infrared spectroscopy can be obtained by considering network width, depth, and parameters.
Original language | English (US) |
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Journal | IEEE Transactions on Cybernetics |
DOIs | |
State | Accepted/In press - 2021 |
All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering
Keywords
- Blood
- Brain modeling
- Brain workload
- cognitive state
- Fatigue
- Gamma distribution
- Matrix decomposition
- Neurons
- non-negative matrix network
- Nonhomogeneous media
- pilots' fatigue
- Wigner-Ville distribution.