Robust Fast Subclass Discriminant Analysis

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

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.
Original languageEnglish
Title of host publication2020 28th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages1397-1401
Number of pages5
ISBN (Electronic)978-9-0827-9705-3
DOIs
Publication statusPublished - 2021
Publication typeA4 Article in conference proceedings
EventEuropean Signal Processing Conference (EUSIPCO) - Amsterdam, Netherlands
Duration: 18 Jan 202121 Jan 2021

Publication series

NameEuropean Signal Processing Conference
ISSN (Electronic)2076-1465

Conference

ConferenceEuropean Signal Processing Conference (EUSIPCO)
Country/TerritoryNetherlands
CityAmsterdam
Period18/01/2121/01/21

Keywords

  • Training
  • Dimensionality reduction
  • Signal processing algorithms
  • Europe
  • Signal processing
  • Robustness
  • Task analysis
  • subclass discriminant analysis
  • subspace learning
  • dimensionality reduction

Publication forum classification

  • Publication forum level 1

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