@inproceedings{1feb0b37024740dfa82e1d18a36ee634,
title = "Target robust discriminant analysis",
abstract = "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.",
keywords = "Domain adaptation, Robust estimators, Discriminant analysis, Robustness",
author = "Kouw, {Wouter M.} and Marco Loog",
year = "2021",
month = apr,
day = "10",
doi = "https://doi.org/10.1007/978-3-030-73973-7_1",
language = "English",
isbn = "978-3-030-73972-0",
series = "Lecture Notes in Computer Science (LNCS)",
publisher = "Springer Nature",
pages = "3--13",
editor = "Andrea Torsello and Luca Rossi and Marcello Pelillo and Battista Biggio and Antonio Robles-Kelly",
booktitle = "Structural, Syntactic, and Statistical Pattern Recognition",
address = "Singapore",
note = "Joint IAPR International Workshops, S+SSPR 2020, Padua, Italy, January 21–22, 2021 ; Conference date: 21-01-2021 Through 22-01-2021",
}