RKUM: An R Package for Robust Kernel Unsupervised Methods
This work provides a tool for researchers and practitioners in machine learning and statistics to perform robust kernel-based analysis on high-dimensional, noisy datasets, but it is incremental as it builds on existing kernel methods with robustness enhancements.
The authors tackled the problem of robust unsupervised learning with kernel methods under contaminated data by developing an R package (RKUM) that implements robust kernel covariance operators and canonical correlation analysis, demonstrating reduced sensitivity to outliers and effective outlier detection in experiments.
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.