A Bayesian nonparametric model for multi-label learning
A Bayesian nonparametric model for multi-label learning
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DOI：
10.1007/s10994-017-5638-4
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发表时间：
2017-08
期刊：
影响因子：
7.5
通讯作者：
Junyu Xuan;Jie Lu;Guangquan Zhang;R. Xu;Xiangfeng Luo
中科院分区：
文献类型：
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作者：
Junyu Xuan;Jie Lu;Guangquan Zhang;R. Xu;Xiangfeng Luo
在过去的几年里，多标签学习由于其广泛的应用场景和研究人员在这一领域开发的越来越多的技术而成为一种重要的学习范式。在现有的研究中，生成统计模型通过学习低维标签嵌入，具有良好的泛化能力和对大量标签的稳健性。然而，这类模型的一个问题是需要预先确定维度的数量，这在许多现实世界的设置中是困难和不合适的。在本文中，我们提出了一个贝叶斯非参数模型来解决这个问题。更具体地说，我们将Gamma负二项过程扩展到三个级别，以捕获标签-实例-特征结构。此外，设计了一种Gamma过程的混合策略，以解决实例的多个标签。这种混合过程也导致了模型推理的困难，因此提出了一种高效的Gibbs抽样推理算法来解决这一困难。在几个真实数据集上的实验表明，该模型在多标签学习任务上的性能与文献中的三个最新模型进行了比较。
Multi-label learning has become a significant learning paradigm in the past few years due to its broad application scenarios and the ever-increasing number of techniques developed by researchers in this area. Among existing state-of-the-art works, generative statistical models are characterized by their good generalization ability and robustness on large number of labels through learning a low-dimensional label embedding. However, one issue of this branch of models is that the number of dimensions needs to be fixed in advance, which is difficult and inappropriate in many real-world settings. In this paper, we propose a Bayesian nonparametric model to resolve this issue. More specifically, we extend a Gamma-negative binomial process to three levels in order to capture the label-instance-feature structure. Furthermore, a mixing strategy for Gamma processes is designed to account for the multiple labels of an instance. The mixed process also leads to a difficulty in model inference, so an efficient Gibbs sampling inference algorithm is then developed to resolve this difficulty. Experiments on several real-world datasets show the performance of the proposed model on multi-label learning tasks, comparing with three state-of-the-art models from the literature.