Extreme Learning Machine: A Faster Alternative to Support Vector Machines

Extreme Learning Machine (ELM) is a machine learning algorithm that demonstrates faster training speeds compared to traditional Support Vector Machines (SVM). It has broad applications in the field of machine learning and excels particularly in handling large-scale datasets. ELM accelerates the training process by utilizing randomly generated input weights and biases during model initialization, which eliminates the need for iterative tuning of these parameters. This approach allows the hidden layer output matrix to be computed directly, with the output weights then determined through a simple least-squares solution (typically implemented via Moore-Penrose pseudoinverse). Unlike SVMs that rely on complex quadratic programming optimization, ELM's implementation involves straightforward matrix operations that can be efficiently executed using libraries like NumPy (e.g., numpy.linalg.pinv() for pseudoinverse calculations). Despite its simplified training mechanism, ELM maintains competitive accuracy levels, making it a preferred algorithm for many researchers and practitioners working with high-dimensional and large-volume data. Key advantages include reduced computational complexity (O(n³) for matrix inversion vs. SVM's O(n³) for kernel methods) and native support for parallelization through matrix decomposition techniques.