RKUM：一个用于鲁棒核无监督方法的R包
RKUM: An R Package for Robust Kernel Unsupervised Methods
AI总结：
本文介绍R包RKUM，它基于广义损失函数实现鲁棒核协方差算子与核CCA，并提供影响函数检测离群点，为高维数据鲁棒核分析提供高效可扩展平台。
摘要：
RKUM是一个为实施鲁棒核无监督方法而开发的R包。它提供了使用广义损失函数（而非传统的二次损失）来估计鲁棒核协方差算子（CO）和鲁棒核互协方差算子（CCO）的函数。这些算子构成了鲁棒核学习的基础，并能在受污染或含噪声的数据条件下实现可靠分析。该包包含鲁棒核典型相关分析（Kernel CCA）的实现，以及针对标准核CCA和多核CCA框架的影响函数（IF）。影响函数量化敏感性，有助于在双视图和多视图数据集中检测有影响力的观测值或离群点。使用合成的双视图和多视图数据进行的实验表明，标准核CCA的影响函数能有效识别离群点，而RKUM中实现的鲁棒核方法对污染表现出降低的敏感性。总体而言，RKUM为高维数据应用中的鲁棒核分析提供了一个高效且可扩展的平台。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
