Multi-objective genetic programming for manifold learning: balancing quality and dimensionality
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- @Article{Lensen:GPEM:H2019,
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author = "Andrew Lensen and Mengjie Zhang and Bing Xue",
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title = "Multi-objective genetic programming for manifold
learning: balancing quality and dimensionality",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2020",
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volume = "21",
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number = "3",
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pages = "399--431",
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month = sep,
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note = "Special Issue: Highlights of Genetic Programming 2019
Events",
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keywords = "genetic algorithms, genetic programming, Manifold
learning, Dimensionality reduction, Feature
construction",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-020-09375-4",
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size = "33 pages",
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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
Citations