A Generalization Error Bound for Multi-class Domain Generalization

Domain generalization is the problem of assigning labels to an unlabeled data set, given several similar data sets for which labels have been provided. Despite considerable interest in this problem over the last decade, there has been no theoretical analysis in the setting of multi-class classificat...

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Bibliographic Details
Published inarXiv.org
Main Authors Aniket Anand Deshmukh, Yunwen Lei, Sharma, Srinagesh, Dogan, Urun, Cutler, James W, Clayton, Scott
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 24.05.2019
Subjects
Algorithms
Classification
Labels
Machine learning
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Summary:Domain generalization is the problem of assigning labels to an unlabeled data set, given several similar data sets for which labels have been provided. Despite considerable interest in this problem over the last decade, there has been no theoretical analysis in the setting of multi-class classification. In this work, we study a kernel-based learning algorithm and establish a generalization error bound that scales logarithmically in the number of classes, matching state-of-the-art bounds for multi-class classification in the conventional learning setting. We also demonstrate empirically that the proposed algorithm achieves significant performance gains compared to a pooling strategy.
ISSN:2331-8422