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 ( Um $^2$2 l ) framework to exploit multiple types of metrics with respect to overdetermined similarities between linkages. In Um $^2$2 l , types of combination operators are 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 Um $^2$2 l , and the theoretical analysis reflects the generalization ability of Um $^2$2 l as well. Extensive experiments on diverse applications exhibit the superior classification performance and comprehensibility of Um $^2$2 l . Visualization results also validate its ability to physical meanings discovery.

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什么使对象相似：统一的多指标学习方法

链接本质上是由可以从多个角度得出的相似性度量确定的。例如，空间链接通常是基于异构数据的位置生成的。但是，语义联系可以来自更多的属性，例如社会关系背后的不同物理含义。许多现有的度量学习模型都将重点放在空间联系上，但是富有的没有考虑语义因素。我们提出了统一的多指标学习（ 嗯 $ ^ 2 $2个 升 ）开发框架 多种类型关于链接之间过高确定的相似性的度量标准。在嗯 $ ^ 2 $2个 升 引入了多种类型的组合运算符以从多个角度进行距离表征，因此可以引入灵活性，以表示和利用空间和语义链接。此外，我们提出了一个统一的求解器嗯 $ ^ 2 $2个 升 ，理论分析反映了泛化能力。 嗯 $ ^ 2 $2个 升也一样 广泛的针对各种应用的实验展示了卓越的分类性能和可理解性嗯 $ ^ 2 $2个 升 。可视化结果还验证了其物理意义发现的能力。