IDEAS home Printed from https://ideas.repec.org/a/spr/advdac/v17y2023i1d10.1007_s11634-021-00486-z.html
   My bibliography  Save this article

Optimal projections for Gaussian discriminants

Author

Listed:
  • David P. Hofmeyr

    (Stellenbosch University)

  • Francois Kamper

    (Stellenbosch University)

  • Michail C. Melonas

    (Stellenbosch University)

Abstract

We study the problem of obtaining optimal projections for performing discriminant analysis with Gaussian class densities. Unlike in most existing approaches to the problem, we focus on the optimisation of the multinomial likelihood based on posterior probability estimates, which directly captures discriminability of classes. Finding optimal projections offers utility for dimension reduction and regularisation, as well as instructive visualisation for better model interpretability. Practical applications of the proposed approach show that it is highly competitive with existing Gaussian discriminant models. Code to implement the proposed method is available in the form of an R package from https://github.com/DavidHofmeyr/OPGD.

Suggested Citation

  • David P. Hofmeyr & Francois Kamper & Michail C. Melonas, 2023. "Optimal projections for Gaussian discriminants," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(1), pages 43-73, March.
    • Handle: RePEc:spr:advdac:v:17:y:2023:i:1:d:10.1007_s11634-021-00486-z
    DOI: 10.1007/s11634-021-00486-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11634-021-00486-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11634-021-00486-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mu Zhu, 2006. "Discriminant analysis with common principal components," Biometrika, Biometrika Trust, vol. 93(4), pages 1018-1024, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:advdac:v:17:y:2023:i:1:d:10.1007_s11634-021-00486-z. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.