In many real-world tasks, particularly those involving data objects with complicated semantics such as images and texts, one object can be represented by multiple instances and simultaneously be associated with multiple labels. Such tasks can be formulated as multi-instance multi-label learning (MIML) problems, and have been extensively studied during the past few years. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data. To efficiently handle large data sets, in this paper we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output label semantics.

中文翻译：

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快速多实例多标签学习


在许多现实世界的任务中，特别是那些涉及具有复杂语义的数据对象（例如图像和文本）的任务中，一个对象可以由多个实例表示，并同时与多个标签相关联。此类任务可以表述为多实例多标签学习（MIML）问题，并且在过去几年中得到了广泛的研究。现有的 MIML 方法在许多应用中都非常有用；然而，它们中的大多数只能处理中等大小的数据。为了有效地处理大型数据集，在本文中，我们提出了 MIMLfast 方法，该方法首先构建所有标签共享的低维子空间，然后训练标签特定的线性模型，通过随机梯度下降来优化近似排名损失。尽管 MIML 问题很复杂，但 MIMLfast 通过利用共享空间的标签关系并发现复杂标签的子概念，能够实现出色的性能。实验表明，MIMLfast 的性能与最先进的技术相比具有很强的竞争力，而其时间成本却少得多。此外，我们的方法能够识别每个标签最具代表性的实例，从而提供理解输入模式和输出标签语义之间关系的机会。