Target robust discriminant analysis

Wouter M. Kouw, Marco Loog

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

1 Citation (Scopus)


In practice, the data distribution at test time often differs, to a smaller or larger extent, from that of the original training data. Consequentially, the so-called source classifier, trained on the available labelled data, deteriorates on the test, or target, data. Domain adaptive classifiers aim to combat this problem, but typically assume some particular form of domain shift. Most are not robust to violations of domain shift assumptions and may even perform worse than their non-adaptive counterparts. We construct robust parameter estimators for discriminant analysis that guarantee performance improvements of the adaptive classifier over the non-adaptive source classifier.

Original languageEnglish
Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition
Subtitle of host publicationJoint IAPR International Workshops, S+SSPR 2020, Proceedings
EditorsAndrea Torsello, Luca Rossi, Marcello Pelillo, Battista Biggio, Antonio Robles-Kelly
Place of PublicationCham
PublisherSpringer Nature
Number of pages11
ISBN (Electronic)978-3-030-73973-7
ISBN (Print)978-3-030-73972-0
Publication statusPublished - 10 Apr 2021
EventJoint IAPR International Workshops, S+SSPR 2020, Padua, Italy, January 21–22, 2021 - Padua, Italy
Duration: 21 Jan 202122 Jan 2021

Publication series

NameLecture Notes in Computer Science (LNCS)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
Name Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)


ConferenceJoint IAPR International Workshops, S+SSPR 2020, Padua, Italy, January 21–22, 2021


  • Domain adaptation
  • Robust estimators
  • Discriminant analysis
  • Robustness


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