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Research paper analyzes spectral stability of pseudoinverse-based Extreme Learning Machines

By PulseAugur Editorial · [2 sources] ·

This research paper investigates the spectral stability of Extreme Learning Machines (ELMs) that utilize pseudoinverse-based methods for computing output weights. The study demonstrates that the smallest singular value of the hidden layer matrix is a critical factor in amplifying perturbations in the output weights, while the condition number quantifies hidden-layer instability. Comparisons between Singular Value Decomposition (SVD)-based pseudoinverse computation and iterative hyperpower methods indicate that SVD-based approaches offer superior reliability, particularly under ill-conditioned scenarios. AI

IMPACT Provides theoretical insights into the numerical stability of a specific machine learning training method.

RANK_REASON The cluster contains an academic paper published on arXiv.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Research paper analyzes spectral stability of pseudoinverse-based Extreme Learning Machines

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Bich Van Nguyen, Ngoc Anh Khong ·

    Spectral Stability of Pseudoinverse-Based Extreme Learning Machine

    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…

  2. arXiv cs.LG TIER_1 English(EN) · Ngoc Anh Khong ·

    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 fro…

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