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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 4/2021

01.06.2020 | Original Article

GMM discriminant analysis with noisy label for each class

verfasst von: Jian-wei Liu, Zheng-ping Ren, Run-kun Lu, Xiong-lin Luo

Erschienen in: Neural Computing and Applications | Ausgabe 4/2021

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Abstract

Real-world datasets often contain noisy labels, and learning from such datasets using standard classification approaches may not produce the desired performance. In this paper, we propose a Gaussian Mixture Discriminant Analysis (GMDA) with noisy label for each class. We introduce flipping probability and class probability and use EM algorithms to solve the discriminant problem with label noise. We also provide the detail proofs of convergence. Experimental results on synthetic and real-world datasets show that the proposed approach notably outperforms other four state-of-the-art methods.
Vorheriger Artikel A two-step abstractive summarization model with asynchronous and enriched-information decoding
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Metadaten
Titel
GMM discriminant analysis with noisy label for each class
verfasst von
Jian-wei Liu
Zheng-ping Ren
Run-kun Lu
Xiong-lin Luo
Publikationsdatum
01.06.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 4/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05038-8

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