Genetic Programming for Explainable Manifold Learning

arXiv:2403.14139v1 Announce Type: cross
Abstract: Manifold learning techniques play a pivotal role in machine learning by revealing lower-dimensional embeddings within high-dimensional data, thus enhancing both the efficiency and interpretability of data analysis by transforming the data into a lower-dimensional representation. However, a notable challenge with current manifold learning methods is their lack of explicit functional mappings, crucial for explainability in many real-world applications. Genetic programming, known for its interpretable functional tree-based models, has emerged as a promising approach to address …

abstract analysis arxiv challenge cs.lg cs.ne current data data analysis efficiency embeddings genetic programming however interpretability machine machine learning manifold pivotal programming representation role type