RKUM: An R Package for Robust Kernel Unsupervised Methods

TL;DR

RKUM is an R package that implements robust kernel unsupervised methods, including robust covariance operators and kernel CCA, to improve analysis reliability in noisy and contaminated data environments.

Contribution

The paper introduces RKUM, a novel R package offering robust kernel covariance and cross-covariance operators, and robust kernel CCA with influence function analysis, enhancing unsupervised learning under data contamination.

Findings

Robust kernel methods reduce sensitivity to noise and outliers.

Influence function effectively detects influential observations.

Experiments show improved robustness over standard kernel CCA.

Abstract

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.