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- W4294626087 abstract "Partial Label Learning (PLL) is a weakly supervised learning framework where each training instance is associated with more than one candidate label. However, label ambiguity in the case of label imbalance has not been well solved. In this paper, a novel label imbalance-aware algorithm named PL-LIMAP is proposed to effectively solve this problem. Firstly, a partial manifold constrained low-rank representation method is proposed to learn the instance affinity matrix, which can identify instances with the same label by fully mining the manifold structure of the data. Moreover, for making full use of the information hidden in candidate labels, the Jaccard distance is introduced to measure the similarity between candidate labels to obtain the label affinity matrix. The combination of these two affinity matrices can achieve the purpose of disambiguation through complementary information. Meanwhile, aiming to determine the imbalance of ambiguous labels, specialized measures are proposed to evaluate the imbalance level in PLL. According to the imbalance level of different labels, an alternative weighting scheme is proposed to mitigate the imbalance of ambiguous labels. As a consequence, the ground-truth label of an instance can be found out even though its label information is not available in advance. Experiments reveal the overwhelming performances of PL-LIMAP." @default.
- W4294626087 created "2022-09-05" @default.
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- W4294626087 date "2022-10-01" @default.
- W4294626087 modified "2023-10-14" @default.
- W4294626087 title "Addressing label ambiguity imbalance in candidate labels: Measures and disambiguation algorithm" @default.
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- W4294626087 doi "https://doi.org/10.1016/j.ins.2022.07.175" @default.
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