A key objective in multiview learning is to model the information common to multiple parallel views of a class of objects/events to improve downstream tasks such as classification and clustering. In this context, two open research challenges remain; achieving scalability: how can we incorporate information from hundreds of views per event into a model? and being view-agnostic: how to learn robust multiview representations without knowledge of how these views are acquired? In this work, we study a neural method based on multiview correlation to capture the information shared across a large number of views by subsampling them in a view-agnostic manner during training. We analyze the error of this bootstrapped multiview correlation objective using matrix concentration theory to provide an upper bound on the number of views to subsample for a given embedding dimension. Our experiments on a diverse set of audio and visual tasks—multi-channel acoustic activity classification, spoken word recognition, 3D object classification, and pose-invariant face recognition—demonstrate the robustness of view bootstrapping to model a large number of views. Results and analysis underscore the applicability of our method for a view-agnostic learning setting.

中文翻译：

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使用视图引导的广义多视图共享子空间学习


多视图学习的一个关键目标是对一类对象/事件的多个并行视图所共有的信息进行建模，以改进分类和聚类等下游任务。在这种背景下，仍然存在两个开放的研究挑战；实现可扩展性：我们如何将每个事件的数百个视图的信息合并到一个模型中？与视图无关：如何在不知道如何获取这些视图的情况下学习鲁棒的多视图表示？在这项工作中，我们研究了一种基于多视图相关性的神经方法，通过在训练期间以与视图无关的方式对它们进行二次采样来捕获大量视图之间共享的信息。我们使用矩阵集中理论来分析这种自举多视图相关目标的误差，以提供给定嵌入维度的子采样视图数量的上限。我们对各种音频和视觉任务（多通道声学活动分类、口语识别、3D 对象分类和姿势不变人脸识别）进行的实验证明了视图引导对大量视图进行建模的鲁棒性。结果和分析强调了我们的方法对于与视图无关的学习环境的适用性。