In this paper, we propose novel methods to address the challenges of dimensionality reduction related to potential outlier classes and imbalanced classes often present in data. In particular, we propose extensions to Fast Subclass Discriminant Analysis and Subclass Discriminant Analysis that allow to put more attention on uder-represented classes or classes that are likely to be confused with each other. Furthermore, the kernelized variants of the proposed algorithms are presented. The proposed methods lead to faster training time and improved accuracy as shown by experiments on eight datasets of different domains, tasks, and sizes.
|Nimi||European Signal Processing Conference|
|Conference||European Signal Processing Conference (EUSIPCO)|
|Ajanjakso||18/01/21 → 21/01/21|