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Learning From M-Tuple One-vs-All Confidence Comparison Data
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Summary
Researchers propose preferred-label partial-label learning in a one-vs-all view (PLL-OVA), where each training instance has a candidate label set plus a preferred label within it. They model preferred-label generation with a noisy-channel formulation covering symmetric and asymmetric mis-selection and missing true labels, and build empirical risk minimization procedures with risk-correction functions such as ReLU and ABS. Experiments on benchmark datasets report that PLL-OVA consistently outperforms standard PLL baselines, especially under large candidate ambiguity or nonuniform mis-selection.