A Critical Analysis of the Theoretical Framework of the Extreme Learning Machine
This work is incremental as it critically analyzes and refines the theoretical framework of ELM, addressing concerns for researchers in machine learning theory.
The authors tackled the theoretical underpinnings of the Extreme Learning Machine (ELM) by refuting its foundational proofs and providing a counterexample dataset, concluding that ELM lacks rigorous mathematical justification but can be efficient in some theoretical cases.
Despite the number of successful applications of the Extreme Learning Machine (ELM), we show that its underlying foundational principles do not have a rigorous mathematical justification. Specifically, we refute the proofs of two main statements, and we also create a dataset that provides a counterexample to the ELM learning algorithm and explain its design, which leads to many such counterexamples. Finally, we provide alternative statements of the foundations, which justify the efficiency of ELM in some theoretical cases.