Multiple instance learning with bag dissimilarities
Multiple instance learning with bag dissimilarities
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DOI：
10.1016/j.patcog.2014.07.022
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发表时间：
2015-01-01
影响因子：
8
通讯作者：
Loog, Marco
中科院分区：
文献类型：
--
作者：
Cheplygina, Veronika;Tax, David M. J.;Loog, Marco
多实例学习（MIL）关注的是从对象集（袋）（实例）中学习，其中单个实例标签是模糊的。在这种情况下，监督学习不能直接应用。通常，专门的MIL方法通过对袋标签和实例标签之间的关系做出额外的假设来学习。这样的假设可能适合特定的数据集，但不能推广到MIL问题的整个范围。其他MIL方法将假设的重点从标签转移到袋子的整体（非）相似性上，因此直接从袋子中学习。我们建议用训练集中每个袋子与其他袋子的不同之处的向量来表示每个袋子，并将这些不同之处视为特征表示。我们展示了几种定义包之间不相似性的替代方法，并讨论了哪些定义更适合特定的MIL问题。实验结果表明，所提出的方法计算成本低，但在广泛的MIL数据集上与最先进的算法非常有竞争力。(C) 2014 Elsevier Ltd.版权所有。
Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods learn by making additional assumptions about the relationship of the bag labels and instance labels. Such assumptions may fit a particular dataset, but do not generalize to the whole range of MIL problems. Other MIL methods shift the focus of assumptions from the labels to the overall (dis)similarity of bags, and therefore learn from bags directly. We propose to represent each bag by a vector of its dissimilarities to other bags in the training set, and treat these dissimilarities as a feature representation. We show several alternatives to define a dissimilarity between bags and discuss which definitions are more suitable for particular MIL problems. The experimental results show that the proposed approach is computationally inexpensive, yet very competitive with state-of-the-art algorithms on a wide range of MIL datasets. (C) 2014 Elsevier Ltd. All rights reserved.