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

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@Article{Lensen:GPEM:H2019,

• author = "Andrew Lensen and Mengjie Zhang and Bing Xue",
• title = "Multi-objective genetic programming for manifold learning: balancing quality and dimensionality",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2020",
• volume = "21",
• number = "3",
• pages = "399--431",
• month = sep,
• note = "Special Issue: Highlights of Genetic Programming 2019 Events",
• keywords = "genetic algorithms, genetic programming, Manifold learning, Dimensionality reduction, Feature construction",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-020-09375-4",
• size = "33 pages",
• abstract = "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...",

}

Genetic Programming entries for Andrew Lensen Mengjie Zhang Bing Xue

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