发表机构
IITRPR（印度理工学院皮拉尼分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多视图分类，提出XGRVFL-MV模型，构建各视图RVFL表示，结合图嵌入与特定损失函数，引入残差耦合项，通过无逆一阶优化求解，在多基准数据集上实验，展现出有竞争力的分类性能。
AI 中文摘要
随机向量函数链接（RVFL）网络为分类提供了一个有效的随机学习框架。现有的多视图RVFL方法利用来自多个视图的互补信息。然而，保留视图特定的几何结构、限制大预测残差的影响以及对多个视图之间的关系进行建模仍然具有挑战性。本文提出了一种具有灵活保护损失的残差耦合图嵌入多视图RVFL模型（XGRVFL-MV）用于多视图分类。该模型为每个视图构建RVFL表示，将图嵌入与使用局部Fisher判别分析加权方案构建的内在图和惩罚图相结合。它还使用有界且不对称的灵活保护（XG）损失进行残差学习。引入了一个残差耦合项，以鼓励视图特定预测残差之间的一致性，同时保留视图特定的表示。使用基于Nesterov加速梯度下降的无逆一阶优化过程来解决由此产生的优化问题。我们在UCI、KEEL、AwA和Corel5k基准数据集上评估了所提出的模型。实验结果以及统计分析和超参数敏感性分析表明，与基准方法相比，XGRVFL-MV在评估的基准数据集上实现了有竞争力的分类性能。
英文摘要
Random Vector Functional Link (RVFL) networks provide an efficient randomized learning framework for classification. Existing multi-view RVFL methods utilize complementary information from multiple views. However, preserving view-specific geometric structure, limiting the influence of large prediction residuals, and modeling relationships between multiple views remain challenging. This paper proposes a Residual-Coupled Graph-Embedded Multi-View RVFL model with fleXi guardian loss (XGRVFL-MV) for multi-view classification. The proposed model constructs RVFL representation for each view, incorporates graph embedding with intrinsic and penalty graphs constructed using the Local Fisher Discriminant Analysis weighting scheme. It also uses the bounded and asymmetric FleXi Guardian (XG) loss for residual learning. A residual-coupling term is introduced to encourage consistency among view-specific prediction residuals while preserving view-specific representations. The resulting optimization problem is solved using an inversion-free first-order optimization procedure based on Nesterov accelerated gradient descent. We evaluate the proposed model on UCI, KEEL, AwA, and Corel5k benchmark datasets. Experimental results, together with statistical analyses and hyperparameter sensitivity analyses, show that XGRVFL-MV achieves competitive classification performance compared with the baseline methods across the evaluated benchmark datasets.
