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HERA
ACM Transactions on Intelligent Systems and Technology ( IF 5 ) Pub Date : 2020-04-03 , DOI: 10.1145/3379501
Gengyu Lyu 1 , Songhe Feng 1 , Yidong Li 1 , Yi Jin 1 , Guojun Dai 2 , Congyan Lang 1
Affiliation  

Partial label learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with this type of problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this article, we propose a novel PLL approach named HERA, which simultaneously incorporates the HeterogEneous Loss and the SpaRse and Low-rAnk procedure to estimate the labeling confidence for each instance while training the desired model. Specifically, the heterogeneous loss integrates the strengths of both the pairwise ranking loss and the pointwise reconstruction loss to provide informative label ranking and reconstruction information for label identification, whereas the embedded sparse and low-rank scheme constrains the sparsity of ground-truth label matrix and the low rank of noise label matrix to explore the global label relevance among the whole training data, for improving the learning model. Comprehensive ablation study demonstrates the effectiveness of our employed heterogeneous loss, and extensive experiments on both artificial and real-world datasets demonstrate that our method achieves superior or comparable performance against state-of-the-art methods.

中文翻译:

赫拉

部分标签学习(PLL)旨在从每个训练实例与一组候选标签相关联的数据中学习,其中只有一个是正确的。大多数现有方法通过平等对待每个候选标签或迭代识别真实标签来处理此类问题。在本文中,我们提出了一种名为 HERA 的新型 PLL 方法,它同时结合了异构损失稀疏和低秩在训练所需模型时估计每个实例的标签置信度的过程。具体来说,异构损失整合了成对排序损失和逐点重建损失的优势,为标签识别提供了信息性标签排序和重建信息,而嵌入式稀疏和低秩方案限制了真实标签矩阵的稀疏性和噪声标签矩阵的低秩来探索整个训练数据之间的全局标签相关性,以改进学习模型。综合消融研究证明了我们采用的异构损失的有效性,并且在人工和真实世界数据集上的广泛实验表明,我们的方法与最先进的方法相比具有优越或可比的性能。
更新日期:2020-04-03
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