论文标题
基于差异性的多视图学习的随机森林
Random Forest for Dissimilarity-based Multi-view Learning
论文作者
论文摘要
许多分类问题自然都是多视图，因为它们的数据是通过多种异质描述来描述的。对于此类任务，差异性策略是使不同描述可比并通过（i）通过（i）为每种观点构建中间差异表示的有效方法，并通过平均观点的差异来融合这些表示形式。在这项工作中，我们表明，随机森林接近度度量可用于构建差异表示，因为此措施反映了特征和阶级成员身份之间的相似之处。然后，我们提出了一种动态视图选择方法，以更好地结合特定视图的差异表示。这允许在每个实例上做出决定，以预测该实例最相关的视图。实验是在几个现实世界的多视图数据集上进行的，并表明，与简单的平均组合和两个最新的静态视图组合相比，动态视图选择可以显着改善性能。
Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions. For such tasks, dissimilarity strategies are effective ways to make the different descriptions comparable and to easily merge them, by (i) building intermediate dissimilarity representations for each view and (ii) fusing these representations by averaging the dissimilarities over the views. In this work, we show that the Random Forest proximity measure can be used to build the dissimilarity representations, since this measure reflects similarities between features but also class membership. We then propose a Dynamic View Selection method to better combine the view-specific dissimilarity representations. This allows to take a decision, on each instance to predict, with only the most relevant views for that instance. Experiments are conducted on several real-world multi-view datasets, and show that the Dynamic View Selection offers a significant improvement in performance compared to the simple average combination and two state-of-the-art static view combinations.
