Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
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DOI：
10.1609/aaai.v33i01.33015508
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Yuying Xing;Guoxian Yu;C. Domeniconi;J. Wang;Z. Zhang;Maozu Guo
中科院分区：
文献类型：
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作者：
Yuying Xing;Guoxian Yu;C. Domeniconi;J. Wang;Z. Zhang;Maozu Guo
多视图多实例多标签学习(M3L)处理包含不同实例的复杂对象，用不同的特征视图表示，并用多个标签进行标注。现有的M3L解决方案只部分探索对象(或包)、实例和标签之间的内部或内部关系，这可以为M3L传达重要的上下文信息。在本文中，我们提出了一种基于协作矩阵分解的解决方案M3Lcmf。M3Lcmf首先使用由包、实例和标签节点组成的异质网络，通过多个关系数据矩阵对不同类型的关系进行编码。为了保持数据矩阵的内在结构，M3Lcmf协作地将它们分解成低阶矩阵，探索包、实例和标签之间的潜在关系，并选择性地合并数据矩阵。在此基础上，提出了一种聚合方案，将实例级标签聚合为袋级标签，并指导分解。在基准数据集上的实证研究表明，M3Lcmf在实例级和BAG级预测上都优于其他相关竞争解决方案。
Multi-view Multi-instance Multi-label Learning (M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which can convey important contextual information for M3L. As such, they may have a compromised performance.\ In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf. M3Lcmf first uses a heterogeneous network composed of nodes of bags, instances, and labels, to encode different types of relations via multiple relational data matrices. To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices. An aggregation scheme is further introduced to aggregate the instance-level labels into bag-level and to guide the factorization. An empirical study on benchmark datasets show that M3Lcmf outperforms other related competitive solutions both in the instance-level and bag-level prediction.