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• W3033127816 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-art methods." @default.
• W3033127816 created "2020-06-12" @default.
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• W3033127816 date "2022-01-25" @default.
• W3033127816 modified "2023-09-23" @default.
• W3033127816 title "GMM Discriminant Analysis with Noisy Label for Each Class" @default.
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• W3033127816 doi "https://doi.org/10.48550/arxiv.2201.10242" @default.
• W3033127816 hasPublicationYear "2022" @default.
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