Multi-class classification in the presence of labelling errors

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Learning kernel logistic regression in the presence of class label noise (2014, methodology): “The rKFD was later extended to multi-class setting by [3] and this has further motivated the recent development of a label noise-tolerant Hidden Markov Model to improve segmentation [15].”
Confident Learning: Estimating Uncertainty in Dataset Labels (2019, methodology): “…crowd-sourced labels by multiple workers (Zhang et al., 2017b; Dawid and Skene, 1979; Ratner et al., 2016), (5) factorization methods for distillation (Li et al., 2017) similar to using SVD for imputation (Amjad et al., 2017), among other methods (Bootkrajang and Kab, 2011; Sáez et al., 2014).”
Classification in the Presence of Label Noise: A Survey (2014, background): “More recently, the same model has been extended to multiclass datasets [252] and sequential data [253].”
Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs (2011, background): “More recently the same model has been extended to multiclass datasets [10].”