| Título: | Deep Fisher Discriminant Analysis |
|---|---|
| Autores: | David Díaz-Vico, Adil OmariAlberto Torres-Barrán, José Ramón Dorronsoro |
| Año: | 2017 |
| Enlace: |
https://link.springer.com/chapter/10.1007%2F978-3-319-59147-6_43 |
Abstract
Fisher Discriminant Analysis’ linear nature and the usual eigen-analysis approach to its solution have limited the application of its underlying elegant idea. In this work we will take advantage of some recent partially equivalent formulations based on standard least squares regression to develop a simple Deep Neural Network (DNN) extension of Fisher’s analysis that greatly improves on its ability to cluster sample projections around their class means while keeping these apart. This is shown by the much better accuracies and g scores of class mean classifiers when applied to the features provided by simple DNN architectures than what can be achieved using Fisher’s linear ones.
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