Direct calculation of out-of-sample predictions in multi-class kernel FDA

Treder, Matthias ORCID: https://orcid.org/0000-0001-5955-2326 2019. Direct calculation of out-of-sample predictions in multi-class kernel FDA. Presented at: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 24-26 April 2019. ESANN 2019 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN, pp. 245-250.

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Abstract

After a two-class kernel Fisher Discriminant Analysis (KFDA) has been trained on the full dataset, matrix inverse updates allow for the direct calculation of out-of-sample predictions for different test sets. Here, this approach is extended to the multi-class case by casting KFDA in an Optimal Scoring framework. In simulations using 10-fold cross-validation and permutation tests the approach is shown to be more than 1000x faster than retraining the classifier in each fold. Direct out-of-sample predictions can be useful on large datasets and in studies with many training-testing iterations.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	ESANN
ISBN:	9782875870650
Date of First Compliant Deposit:	23 August 2019
Last Modified:	26 Oct 2022 07:32
URI:	https://orca.cardiff.ac.uk/id/eprint/125095

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