Extreme Learning Machine (ELM): Algorithm Overview and Implementation

Extreme Learning Machine (ELM) is a remarkably simple, efficient learning algorithm for Single-hidden Layer Feedforward Neural Networks (SLFNs). Originally proposed by Associate Professor Guang-Bin Huang at Nanyang Technological University in 2006, ELM significantly differs from traditional neural network algorithms like Backpropagation (BP). Unlike BP requiring manual tuning of numerous parameters (learning rate, momentum) and prone to local minima, ELM necessitates only the configuration of hidden layer nodes. During execution, input weights and hidden layer biases are randomly initialized and remain fixed, while output weights are analytically determined through Moore-Penrose pseudoinverse computation. This approach guarantees unique global optimal solutions, delivering faster training speeds (often orders of magnitude quicker than iterative methods) and superior generalization capabilities. Code implementation typically involves: 1) Random weight/bias initialization, 2) Hidden layer output calculation via activation functions (e.g., sigmoid, ReLU), and 3) Output weight solution using linear matrix operations.