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摘要: 偏标记数据消歧是利用偏标记数据进行机器学习的基础.针对偏标记数据中广泛存在的数据不平衡问题, 以及现有消歧算法对样本间约束信息利用不足的问题, 本文提出一种基于成对约束的偏标记数据消歧算法.首先, 基于低秩表示, 推导出数据不平衡条件下样本低秩表示系数和样本相似度之间的关系; 其次, 基于推导结果, 分别构建基于样本间正约束和负约束的图模型, 通过最小化图模型的能量函数求解偏标记数据的标签.在5个公开数据集上的实验结果表明本文方法相对基准算法在消歧准确率上平均提高了2.9 % ~ 14.9 %.Abstract: Partial label data disambiguation is the basis of machine learning using partial label data. In order to solve the data imbalance problem widely existing in partial label data, and the problem that the existing disambiguation algorithms have insufficient utilization of constraints between samples, a partial label data disambiguation algorithm based on pairwise constraints is proposed in this paper. Firstly, the relation between low-rank representation coefficients and sample similarities in unbalanced datasets is deduced by utilizing low-rank representation. Secondly, according to the deduced results, two graphs are created based on positive constraint and negative constraint respectively. Finally, the labels of partial label data samples are obtained by minimizing energy functions based on graphs. Experimental results on five open datasets indicate that the proposed algorithm outperforms benchmark algorithms by 2.9 % ~ 14.9 % at disambiguation accuracy.
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Key words:
- Partial label data /
- disambiguation /
- imbalanced data /
- low-rank representation /
- pairwise constraints
1) 本文责任编委 王立威 -
图 2 正负约束作用于消歧的效果
Fig. 2 The effects of positive and negative constraints on disambiguation
图 4 数据集中不同类别样本数量分布
Fig. 4 The distributions of different categories$'$ sample number in datasets
图 5 PLDPC消歧准确率随不同参数的变化趋势
Fig. 5 The accuracy of disambiguation changes as different parameters varying
表 1 数据集信息
Table 1 The information of datasets
数据集 样本数量 特征维度 类别数量 平均候选标签数量 领域 Lost 1 122 108 16 2.23 人脸自动标注 MSRCV2 1 758 48 23 3.16 目标分类 BirdSong 4 998 38 13 2.18 鸟鸣分类 Soccer Player 17 472 279 171 2.09 人脸自动标注 Yahoo!News 22 991 163 219 1.91 人脸自动标注 
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表 2 各算法消歧准确率(%)
Table 2 The disambiguation accuracy of each algorithm (%)
算法 Lost MSRCV2 BirdSong Soccer Player Yahoo!News PLDPC-abs 57.93 65.81 76.73 70.28 82.03 PLDPC-$p$ 65.81 67.46 77.05 71.58 83.29 PLDPC-$n$ 41.98 37.26 62.30 62.84 57.17 PL-KNN 64.53 58.25 70.99 57.76 72.32 IPAL 77.54 71.44 76.61 67.35 82.37 PL-LEAF 79.32 66.67 75.55 70.50 82.90 MMS 91.71 68.27 66.47 70.03 87.32 PLDPC 87.61 72.70 79.25 73.68 85.22
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表 3 各算法消歧处理时间(秒(s)、分钟(min)、天(d))
Table 3 The processing time of each algorithm (second (s), minute (min), day (d))
算法 Lost MSRCV2 BirdSong Soccer Player Yahoo!News PLDPC-abs 2.13 s 2.39 s 11.62 s 4 min 6 min PLDPC-$p$ 2.05 s 2.30 s 11.07 s 4 min 6 min PLDPC-$n$ 2.12 s 2.69 s 11.71 s 4 min 6 min PL-KNN 0.06 s 0.08 s 0.10 s 59.27 69.78 s IPAL 0.51 s 0.63 s 1.56 s 73.75 s 94.62 s PL-LEAF 56.04 s 4 min 35 min $>$1 d $>$1 d MMS 57.02 s 1 min 2 min 34 min 35min PLDPC 2.16 s 2.45 s 11.61 s 4 min 6 min
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