Ye Han-Jia, Zhan De-Chuan, Jiang Yuan, Zhou Zhi-Hua
IEEE Trans Pattern Anal Mach Intell. 2018 Apr 20. doi: 10.1109/TPAMI.2018.2829192.
Linkages are essentially determined by similarity measures that may be derived from multiple perspectives. For example, spatial linkages are usually generated based on localities of heterogeneous data. Semantic linkages, however, can come from even more properties, such as different physical meanings behind social relations. Many existing metric learning models focus on spatial linkages but leave the rich semantic factors unconsidered. We propose a Unified Multi-Metric Learning framework to exploit multiple types of metrics with respect to overdetermined similarities between linkages. In , a type of combination operator is introduced for distance characterization from multiple perspectives, and thus can introduce flexibilities for representing and utilizing both spatial and semantic linkages. Besides, we propose a uniform solver for , and the theoretical analysis reflects the generalization ability of as well. Extensive experiments on diverse applications exhibit the superior classification performance and comprehensibility of . Visualization results also validate its ability on physical meanings discovery.
链接本质上由可从多个视角得出的相似性度量决定。例如，空间链接通常基于异构数据的位置生成。然而，语义链接可能来自更多属性，比如社会关系背后不同的物理意义。许多现有的度量学习模型专注于空间链接，但未考虑丰富的语义因素。我们提出一个统一的多度量学习框架，以利用关于链接间超定相似性的多种类型的度量。在该框架中，引入了一种组合算子，用于从多个视角进行距离表征，从而可为表示和利用空间及语义链接引入灵活性。此外，我们为该框架提出了一个统一的求解器，理论分析也反映了其泛化能力。在各种应用上的大量实验展示了该框架卓越的分类性能和可理解性。可视化结果也验证了其在物理意义发现方面的能力。
