Can Genetic Programming Do Manifold Learning Too?
This work addresses the need for interpretable dimensionality reduction in exploratory data analysis, offering a novel method for researchers and practitioners, though it appears incremental as it adapts genetic programming to an existing task.
The paper tackles the problem of interpretability in manifold learning by proposing GP-MaL, a genetic programming approach that evolves functional mappings from high-dimensional to lower-dimensional spaces using interpretable trees, and shows it is competitive with existing algorithms while producing interpretable and reusable models.
Exploratory data analysis is a fundamental aspect of knowledge discovery that aims to find the main characteristics of a dataset. Dimensionality reduction, such as manifold learning, is often used to reduce the number of features in a dataset to a manageable level for human interpretation. Despite this, most manifold learning techniques do not explain anything about the original features nor the true characteristics of a dataset. In this paper, we propose a genetic programming approach to manifold learning called GP-MaL which evolves functional mappings from a high-dimensional space to a lower dimensional space through the use of interpretable trees. We show that GP-MaL is competitive with existing manifold learning algorithms, while producing models that can be interpreted and re-used on unseen data. A number of promising future directions of research are found in the process.