论文标题
在多视图信息瓶颈表示
On the Multi-View Information Bottleneck Representation
论文作者
论文摘要
在这项工作中，我们将信息瓶颈（IB）方法推广到多视图学习环境。最佳代表性的成倍增长的复杂性促进了两种新型配方的发展，并具有更有利的性能复杂性权衡。第一种方法是基于形成随机共识，适用于不同视图之间具有显着{\ em表示重叠}的方案。第二种方法依赖于增量更新，是针对其他极端情况定制的，其表示最小的表示重叠。在这两种情况下，我们都将早期的工作扩展到乘数（ADMM）求解器的交替定向方法，并建立其收敛性和可扩展性。从经验上讲，我们发现在广泛的建模参数下，所提出的方法在多视图分类问题中的最先进方法优于最先进的方法。
In this work, we generalize the information bottleneck (IB) approach to the multi-view learning context. The exponentially growing complexity of the optimal representation motivates the development of two novel formulations with more favorable performance-complexity tradeoffs. The first approach is based on forming a stochastic consensus and is suited for scenarios with significant {\em representation overlap} between the different views. The second method, relying on incremental updates, is tailored for the other extreme scenario with minimal representation overlap. In both cases, we extend our earlier work on the alternating directional methods of multiplier (ADMM) solver and establish its convergence and scalability. Empirically, we find that the proposed methods outperform state-of-the-art approaches in multi-view classification problems under a broad range of modelling parameters.
