1 research outputs found
Can Genetic Programming Do Manifold Learning Too?
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.Comment: 16 pages, accepted in EuroGP '1