GMM Discriminant Analysis with Noisy Label for Each Class. (arXiv:2201.10242v1 [cs.LG])

Web: http://arxiv.org/abs/2201.10242

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 …

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