Green-ELM: Efficient Analytic Learning via High-Dimensional Random Projections
We present Green-ELM, a non-iterative neural architecture that replaces gradient-based optimization of the output layer with a closed-form analytic solution over a fixed, high-dimensional random feature representation. By projecting input manifolds into a high-dimensional, random feature space ($d \gg 784$), our results show that complex class boundaries can be effectively untangled without the computational overhead of backpropagation. Utilizing the Moore-Penrose pseudoinverse, LU and Cholesky decomposition to solve for the output layer in a single analytic step, Green-ELM achieves a classification accuracy of 98.10\% on MNIST ($d=4000$) and 86.63\% on Fashion-MNIST. Furthermore, we expe...
| Authors | Wladimir Silva |
|---|---|
| Primary category | cs.LG |
| Published | 17 April 2026 |
| Last updated at source | 17 September 2026 |
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