A critical analysis of the theoretical framework of the Extreme Learning Machine. - Perfilieva, Irina; Madrid-Labrador, Nicolás Miguel; Ojeda-Aciego, Manuel; Artiemjew, Piotr; Niemczynowicz, Agnieszka (Elsevier, 2025)
Despite several successful applications of the Extreme Learning Machine (ELM) as a new neural network training method that combines random selection with deterministic computation, we show that some fundamental principles of ELM lack a rigorous mathematical basis. In particular, we refute the proofs of two fundamental claims and construct datasets that serve as counterexamples to the ELM algorithm. Finally, we provide alternative claims to the basic principles that justify the effectiveness of ELM in some theoretical cases.
https://openpolicyfinder.jisc.ac.uk/id/publication/15862
Keywords: Aprendizaje automático (Inteligencia artificial); Redes neuronales (Informática); Matrices (Matemáticas); Extreme learning machine; Feed-forward neural network; Pseudo-inverse matrix; Generalized inverse Moore-Penrose matrix