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Can Genetic Programming Do Manifold Learning Too?

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journal contribution
posted on 06.10.2020 by Andrew Lensen, Bing Xue, Mengjie Zhang
© Springer Nature Switzerland AG 2019. 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.


Preferred citation

Lensen, A., Xue, B. & Zhang, M. (n.d.). Can Genetic Programming Do Manifold Learning Too? https://doi.org/10.26686/wgtn.12493802


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