LABEL PROPAGATION FOR LEARNING WITH LABEL PROPORTIONS
LABEL PROPAGATION FOR LEARNING WITH LABEL PROPORTIONS
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DOI：
10.1109/mlsp.2018.8517083
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Rafael Poyiadzi;Raúl Santos-Rodríguez;N. Twomey
中科院分区：
文献类型：
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作者：
Rafael Poyiadzi;Raúl Santos-Rodríguez;N. Twomey
标签比例学习（LLP）是当数据以袋的形式呈现时，恢复给定数据集的底层真实标签的问题。这种范例特别适用于提供单个标签的成本很高，而标签聚合更容易获得的情况。在医疗保健领域，对患者来说，详细记录他们的日常活动是一种负担，但他们通常愿意提供更高层次的日常行为总结。我们提出了一种新颖高效的基于图形的算法，该算法鼓励局部平滑并利用数据的全局结构，同时保留每个包的“质量”。
Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is particularly suitable in contexts where providing individual labels is expensive and label aggregates are more easily obtained. In the healthcare domain, it is a burden for a patient to keep a detailed diary of their daily routines, but often they will be amenable to provide higher level summaries of daily behavior. We present a novel and efficient graph-based algorithm that encourages local smoothness and exploits the global structure of the data, while preserving the ‘mass’ of each bag.