IEEE Trans Pattern Anal Mach Intell. 2021 Oct;43(10):3573-3586. doi: 10.1109/TPAMI.2020.2987013. Epub 2021 Sep 2.
In many real-world scenarios, data from multiple modalities (sources) are collected during a development phase. Such data are referred to as multiview data. While additional information from multiple views often improves the performance, collecting data from such additional views during the testing phase may not be desired due to the high costs associated with measuring such views or, unavailability of such additional views. Therefore, in many applications, despite having a multiview training data set, it is desired to do performance testing using data from only one view. In this paper, we present a multiview feature selection method that leverages the knowledge of all views and use it to guide the feature selection process in an individual view. We realize this via a multiview feature weighting scheme such that the local margins of samples in each view are maximized and similarities of samples to some reference points in different views are preserved. Also, the proposed formulation can be used for cross-view matching when the view-specific feature weights are pre-computed on an auxiliary data set. Promising results have been achieved on nine real-world data sets as well as three biometric recognition applications. On average, the proposed feature selection method has improved the classification error rate by 31 percent of the error rate of the state-of-the-art.
在许多实际场景中，在开发阶段会收集来自多个模态（来源）的数据。此类数据被称为多视图数据。虽然来自多个视图的额外信息通常会提高性能，但由于测量此类视图的成本较高或无法获得此类附加视图，因此在测试阶段可能不希望从这些附加视图中收集数据。因此，在许多应用中，尽管有一个多视图训练数据集，但希望仅使用一个视图的数据进行性能测试。在本文中，我们提出了一种多视图特征选择方法，该方法利用了所有视图的知识，并将其用于指导单个视图中的特征选择过程。我们通过多视图特征加权方案来实现这一点，使得每个视图中的样本的局部边界最大化，并且不同视图中样本与某些参考点的相似性得以保留。此外，当在辅助数据集上预先计算特定于视图的特征权重时，所提出的公式可用于跨视图匹配。在九个真实世界数据集和三个生物识别应用程序上都取得了令人鼓舞的结果。平均而言，所提出的特征选择方法将分类错误率降低了 31%，优于最先进的方法的错误率。
