Spectral Stability of Pseudoinverse-Based Extreme Learning Machine
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 iterative hyperpower methods and discuss width-dependen
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- LinkedLinked via arxiv author · 85%Bich Van Nguyen →
“Spectral Stability of Pseudoinverse-Based Extreme Learning Machine”
- LinkedLinked via arxiv author · 85%Ngoc Anh Khong →
“Spectral Stability of Pseudoinverse-Based Extreme Learning Machine”
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- FuzzySimilar title/name (fuzzy) · 59%EthicalML/awesome-production-machine-learning →
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- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
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