Extreme Learning Machines

Brief Introduction to Extreme Learning Machines (ELM)

Extreme Learning Machines (ELMs) are a type of feedforward neural network known for fast training speed and good generalization performance. The contain 2 active layers. A fixed random layer with activation functions and an entirely linear read-out layer. 

Here's a point-wise introduction:

  • Type: Single-hidden layer feedforward neural networks (SLFNs).

  • Key Idea: Input weights and biases in the hidden layer are randomly assigned and not updated during training.

  • Training: The read-out layer weights are analytically determined using least squares (no iterative backpropagation).  Single shot least square.

  • Advantage: Extremely fast training compared to traditional neural networks.

  • Application Areas: Regression, classification, clustering, and feature learning tasks.

  • Limitation: Performance depends on hidden layer size and random initialization.

Current State of the Art (as of 2025)

  • ELM Variants:

    • Kernel-based ELM (KELM): Uses kernel tricks to enhance learning without explicit hidden layer mapping.

    • Hierarchical/Deep ELM: Stacks of ELMs or deep representations with ELM training at each layer.

    • Incremental/Online ELM: Adapted for streaming data with continual updates.

  • Hybrid Models:

    • ELM + Deep Learning: Used for feature extraction with deep networks, followed by ELM for fast classification.

    • ELM with Metaheuristics: Integration with optimization algorithms (e.g., PSO, GA) for weight selection.

  • Applications in 2025:

    • Edge AI (due to low computational demand).

    • IoT devices for real-time analytics.

    • Biomedical signal analysis and fault detection systems.

  • Performance Benchmark:

    • Still considered competitive for lightweight applications and real-time tasks.

    • Not state-of-the-art for large-scale deep learning tasks but a strong choice for fast, shallow learning problems.

  • Research Trends:

    • Emphasis on interpretability, robustness to noise, and adaptive architectures.

    • Integration with explainable AI methods.

    • Use in federated learning scenarios.


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