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

Treder, Matthias 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: 23 Aug 2019 11:24
URI: http://orca.cf.ac.uk/id/eprint/125095

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