arXiv:2607.08581v1 Announce Type: new Abstract: Extreme Learning Machine (ELM) computes output weights analytically using the Moore-Penrose pseudoinverse. Although this leads to fast training, its numerical stability depends strongly on the conditioning of the hidden layer matrix. This paper studies pseudoinverse-based ELM from a spectral perspective. We show that the smallest singular value governs perturbation amplification in the output weights, while the condition number provides a quantitative measure of hidden-layer instability. We compare SVD-based pseudoinverse computation with iterati
This research is published as AI development continues to seek more efficient and stable learning architectures, pushing the boundaries of computational performance and reliability.
Understanding the spectral stability of ELMs is crucial for developing more robust and industrially viable AI systems, particularly where fast training meets demands for high numerical precision.
The explicit identification of singular values and condition numbers as key indicators for ELM stability offers a clearer pathway to designing more reliable and predictable extreme learning machines.
- · AI researchers
- · Machine learning developers
- · Sectors using real-time predictive models
- · Systems with unstable ELM implementations
Improved theoretical understanding of Extreme Learning Machines' numerical stability.
Development of more reliable and robust AI models for various applications due to enhanced ELM architectures.
Increased adoption of ELM in critical systems where perturbation sensitivity was previously a barrier, potentially accelerating certain AI deployments.
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Read at arXiv cs.LG