R包RKUM让核方法在噪声数据中更稳定，适合高维数据分析。
RKUM: An R Package for Robust Kernel Unsupervised Methods
- 用广义损失替代二次损失，提升核方法抗干扰能力。
- 实验显示其能有效识别双视角与多视角数据中的异常点。
- 适合处理含噪声的高维数据，尤其适用于统计分析与机器学习场景。
RKUM 是一个用于实现鲁棒核无监督方法的 R 包，提供基于广义损失函数而非传统二次损失的鲁棒核协方差算子（CO）和鲁棒核交叉协方差算子（CCO）估计函数。这些算子构成鲁棒核学习的基础，可在污染或噪声数据条件下实现可靠分析。该包实现了鲁棒核典型相关分析（Kernel CCA），并提供了标准与多核 CCA 框架下的影响函数（IF），用于量化敏感性并检测双视角与多视角数据集中的影响点或离群值。使用合成的双视角与多视角数据进行的实验表明，标准 Kernel CCA 的影响函数能有效识别异常点，而 RKUM 中实现的鲁棒核方法对数据污染表现出更低的敏感性。总体而言，RKUM 提供了一个高效且可扩展的平台，适用于高维数据分析中的鲁棒核方法研究。
原文摘要 · Abstract (English)
RKUM is an R package developed for implementing robust kernel-based unsupervised methods. It provides functions for estimating the robust kernel covariance operator (CO) and the robust kernel cross-covariance operator (CCO) using generalized loss functions instead of the conventional quadratic loss. These operators form the foundation of robust kernel learning and enable reliable analysis under contaminated or noisy data conditions. The package includes implementations of robust kernel canonical correlation analysis (Kernel CCA), as well as the influence function (IF) for both standard and multiple kernel CCA frameworks. The influence function quantifies sensitivity and helps detect influential or outlying observations across two-view and multi-view datasets. Experiments using synthesized two-view and multi-view data demonstrate that the IF of the standard kernel CCA effectively identifies outliers, while the robust kernel methods implemented in RKUM exhibit reduced sensitivity to contamination. Overall, RKUM provides an efficient and extensible platform for robust kernel-based analysis in high-dimensional data applications.
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