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Matrix factorization for identifying noisy labels of multi-label instances

conference contribution
posted on 2018-01-01, 00:00 authored by X Chen, G Yu, C Domeniconi, J Wang, Zili ZhangZili Zhang
© Springer International Publishing AG, part of Springer Nature 2018. Current effort on multi-label learning generally assumes that the given labels are noise-free. However, obtaining noise-free labels is quite difficult and often impractical. In this paper, we study how to identify a subset of relevant labels from a set of candidate ones given as annotations to instances, and introduce a matrix factorization based method called MF-INL. It first decomposes the original instance-label association matrix into two low-rank matrices using nonnegative matrix factorization with feature-based and label-based constraints to retain the geometric structure of instances and label correlations. MF-INL then reconstructs the association matrix using the product of the decomposed matrices, and identifies associations with the lowest confidence as noisy associations. An empirical study on real-world multi-label datasets with injected noisy labels shows that MF-INL can identify noisy labels more accurately than other related solutions and is robust to input parameters. We empirically demonstrate that both feature-based and label-based constraints contribute to boosting the performance of MF-INL.

History

Event

Artificial Intelligence. Pacific Rim International Conference (16th : 2018 : Nanjing, China)

Volume

11013

Series

Lecture Notes in Computer Science

Pagination

508 - 517

Publisher

Springer

Location

Nanjing, China

Place of publication

Cham, Switzerland

Start date

2018-08-28

End date

2018-08-31

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319973098

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2018, Springer International Publishing AG

Editor/Contributor(s)

X Geng, B Kang

Title of proceedings

PRICAI 2018: Proceedings of the 16th Pacific Rim International Conference on Artificial Intelligence : Trends in Artificial Intelligence

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