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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Identifying noisy labels with a transductive semi-supervised leave-one-out filter

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Author(s):
de Aquino Afonso, Bruno Klaus [1] ; Berton, Lilian [1]
Total Authors: 2
Affiliation:
[1] Fed Univ Sao Paulo UNIFESP, Inst Sci & Technol, Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: PATTERN RECOGNITION LETTERS; v. 140, p. 127-134, DEC 2020.
Web of Science Citations: 0
Abstract

Obtaining data with meaningful labels is often costly and error-prone. In this situation, semi-supervised learning (SSL) approaches are interesting, as they leverage assumptions about the unlabeled data to make up for the limited amount of labels. However, in real-world situations, we cannot assume that the labeling process is infallible, and the accuracy of many SSL classifiers decreases significantly in the presence of label noise. In this work, we introduce the LGC\_LVOf , a leave-one-out filtering approach based on the Local and Global Consistency (LGC) algorithm. Our method aims to detect and remove wrong labels, and thus can be used as a preprocessing step to any SSL classifier. Given the propagation matrix, detecting noisy labels takes O(cl) per step, with c the number of classes and l the number of labels. Moreover, one does not need to compute the whole matrix, but only al x l submatrix corresponding to interactions between labeled instances. As a result, our approach is best suited to datasets with a large amount of unlabeled data but not many labels. Results are provided for a number of datasets, including MNIST and ISOLET . LGC\_LVOf appears to be equally or more precise than the adapted gradient-based filter, and thus can be used in practice for active learning, where it may iteratively send labels for re-evaluation. We show that the best-case accuracy of the embedding of LGC\_LVOf into LGC yields performance comparable to the best-case of t 1-based classifiers designed to be robust to label noise. (C) 2020 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 18/01722-3 - Semi-supervised learning via complex networks: network construction, selection and propagation of labels and applications
Grantee:Lilian Berton
Support type: Regular Research Grants
FAPESP's process: 18/15014-0 - Label Noise Reduction in Graph-Based Semi-Supervised Learning
Grantee:Bruno Klaus de Aquino Afonso
Support type: Scholarships in Brazil - Master