通过多视图中层融合，提升高维小样本数据的模型性能。
Multi-view mid fusion: a universal approach for learning in an HDLSS setting
- 将高维特征拆分为多个子集作为不同视图，实现中层融合。
- 在多种模型和任务上验证了方法的有效性与泛化能力。
- 适合处理特征远多于样本的高维小样本场景。
高维低样本（HDLSS）设置在众多应用中带来挑战，因特征维度远超可用样本数。本文提出一种通用的多视图中层融合学习方法，适用于HDLSS设置。即使没有天然视图，现有中层融合多视图方法仍表现良好。本文提出三种视图构建方法，将高维特征向量划分为更小的子集，每个子集代表一个视图。在多种模型类型和学习任务上的大量实验验证了该方法的有效性与泛化能力。我们认为，本工作为探索多视图中层融合学习的普遍优势奠定了基础。
原文摘要 · Abstract (English)
The high-dimensional low-sample-size (HDLSS) setting presents significant challenges in various applications where the feature dimension far exceeds the number of available samples. This paper introduces a universal approach for learning in HDLSS setting using multi-view mid fusion techniques. It shows how existing mid fusion multi-view methods perform well in an HDLSS setting even if no inherent views are provided. Three view construction methods are proposed that split the high-dimensional feature vectors into smaller subsets, each representing a different view. Extensive experimental validation across model-types and learning tasks confirm the effectiveness and generalization of the approach. We believe the work in this paper lays the foundation for further research into the universal benefits of multi-view mid fusion learning.
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