不相关半配对子空间学习
Uncorrelated Semi-paired Subspace Learning
摘要：
在许多现实世界的应用中，多视图数据集越来越多地被收集，我们已经看到现有的多视图学习方法比单独应用于每个视图的传统单视图学习方法具有更好的学习性能。但是，大多数这些多视图学习方法都是建立在这样的假设之上的：在每个实例中没有视图丢失，并且来自所有视图的所有数据点必须完美配对。因此，它们不能处理未配对的数据，而是在学习过程中完全忽略它们。然而，在现实中，不成对的数据可能比成对的数据更丰富，简单地忽略所有不成对的数据会造成巨大的资源浪费。在本文中，我们专注于学习不相关的功能，半对子空间学习，动机是许多现有的作品，显示出巨大的成功学习不相关的功能。具体来说，我们提出了一个广义的不相关多视点子空间学习框架，它可以自然地集成许多已证明的学习标准的半配对数据。为了展示框架的灵活性，我们实例化了五个新的半成对模型，用于无监督和半监督学习。我们还设计了一个逐次交替逼近（SAA）的方法来解决由此产生的优化问题，该方法可以结合强大的Krylov子空间投影技术，如果需要的话。多视角特征提取和多模态分类的大量实验结果表明，我们提出的模型表现出竞争力或优于基线。
英文摘要：
Multi-view datasets are increasingly collected in many real-world applications, and we have seen better learning performance by existing multi-view learning methods than by conventional single-view learning methods applied to each view individually. But, most of these multi-view learning methods are built on the assumption that at each instance no view is missing and all data points from all views must be perfectly paired. Hence they cannot handle unpaired data but ignore them completely from their learning process. However, unpaired data can be more abundant in reality than paired ones and simply ignoring all unpaired data incur tremendous waste in resources. In this paper, we focus on learning uncorrelated features by semi-paired subspace learning, motivated by many existing works that show great successes of learning uncorrelated features. Specifically, we propose a generalized uncorrelated multi-view subspace learning framework, which can naturally integrate many proven learning criteria on the semi-paired data. To showcase the flexibility of the framework, we instantiate five new semi-paired models for both unsupervised and semi-supervised learning. We also design a successive alternating approximation (SAA) method to solve the resulting optimization problem and the method can be combined with the powerful Krylov subspace projection technique if needed. Extensive experimental results on multi-view feature extraction and multi-modality classification show that our proposed models perform competitively to or better than the baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
