论文标题
使用查看引导程序的广义多视图共享子空间学习
Generalized Multi-view Shared Subspace Learning using View Bootstrapping
论文作者
论文摘要
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的共有信息进行建模，以改善下游学习任务。在这种情况下，仍然存在两个开放的研究问题：我们如何为每个事件的数百个观点建模？我们可以在不了解这些观点如何获得的情况下学习强大的多视图嵌入吗？我们提出了一种基于多视图相关性的神经方法，可以通过在训练过程中以视图敏锐的方式进行次采样，以捕获大量视图中共享的信息。为了在给定嵌入维度的子样本的视图数量上提供上限，我们使用矩阵浓度理论分析了自举的多视图相关目标的误差。我们对口语识别，3D对象分类和姿势不变的面部识别的实验证明了视图自举的稳健性，以模拟大量视图。结果强调了我们方法在视图不足的学习设置中的适用性。
A key objective in multi-view learning is to model the information common to multiple parallel views of a class of objects/events to improve downstream learning tasks. In this context, two open research questions remain: How can we model hundreds of views per event? Can we learn robust multi-view embeddings without any knowledge of how these views are acquired? We present a neural method based on multi-view correlation to capture the information shared across a large number of views by subsampling them in a view-agnostic manner during training. To provide an upper bound on the number of views to subsample for a given embedding dimension, we analyze the error of the bootstrapped multi-view correlation objective using matrix concentration theory. Our experiments on 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 underscore the applicability of our method for a view-agnostic learning setting.
