Open Access Te Herenga Waka-Victoria University of Wellington
Browse
lensen2019multi.pdf (842.26 kB)

Multi-objective genetic programming for manifold learning: balancing quality and dimensionality

Download (842.26 kB)
journal contribution
posted on 2020-06-16, 22:21 authored by Andrew LensenAndrew Lensen, Mengjie ZhangMengjie Zhang, Bing XueBing Xue
© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of a dataset, manifold learning algorithms can substantially reduce the dimensionality of a dataset while preserving as much information as possible. However, state-of-the-art manifold learning algorithms are opaque in how they perform this transformation. Understanding the way in which the embedding relates to the original high-dimensional space is critical in exploratory data analysis. We previously proposed a Genetic Programming method that performed manifold learning by evolving mappings that are transparent and interpretable. This method required the dimensionality of the embedding to be known a priori, which makes it hard to use when little is known about a dataset. In this paper, we substantially extend our previous work, by introducing a multi-objective approach that automatically balances the competing objectives of manifold quality and dimensionality. Our proposed approach is competitive with a range of baseline and state-of-the-art manifold learning methods, while also providing a range (front) of solutions that give different trade-offs between quality and dimensionality. Furthermore, the learned models are shown to often be simple and efficient, utilising only a small number of features in an interpretable manner.

History

Preferred citation

Lensen, A., Xue, B. & Zhang, M. (2020). Multi-objective genetic programming for manifold learning: balancing quality and dimensionality. Genetic Programming and Evolvable Machines, 1-33. https://doi.org/10.1007/s10710-020-09375-4

Journal title

Genetic Programming and Evolvable Machines

Publication date

2020-01-01

Pagination

1-33

Publisher

Springer Science and Business Media LLC

Publication status

Published

Online publication date

2020-02-05

ISSN

1389-2576

eISSN

1573-7632

Language

en

Usage metrics

    Journal articles

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC