# Influence Function and Robust Variant of Kernel Canonical Correlation Analysis

## TL;DR

This paper develops a robust kernel canonical correlation analysis (CCA) method using influence functions and generalized loss functions, improving robustness to contaminated data and outliers in unsupervised kernel learning.

## Contribution

It introduces a robust kernel covariance and cross-covariance operator, derives their influence functions, and proposes a robust kernel CCA method that outperforms standard kernel CCA on contaminated data.

## Key findings

- Influence function of standard kernel CCA can identify outliers.
- Robust kernel CCA performs better than standard kernel CCA on noisy data.
- Proposed methods are applicable to other kernel-based unsupervised learning techniques.

## Abstract

Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). Both kernel CO and kernel CCO are sensitive to contaminated data, even when bounded positive definite kernels are used. To the best of our knowledge, there are few well-founded robust kernel methods for statistical unsupervised learning. In addition, while the influence function (IF) of an estimator can characterize its robustness, asymptotic properties and standard error, the IF of a standard kernel canonical correlation analysis (standard kernel CCA) has not been derived yet. To fill this gap, we first propose a robust kernel covariance operator (robust kernel CO) and a robust kernel cross-covariance operator (robust kernel CCO) based on a generalized loss function instead of the quadratic loss function. Second, we derive the IF for robust kernel CCO and standard kernel CCA. Using the IF of the standard kernel CCA, we can detect influential observations from two sets of data. Finally, we propose a method based on the robust kernel CO and the robust kernel CCO, called {\bf robust kernel CCA}, which is less sensitive to noise than the standard kernel CCA. The introduced principles can also be applied to many other kernel methods involving kernel CO or kernel CCO. Our experiments on synthesized data and imaging genetics analysis demonstrate that the proposed IF of standard kernel CCA can identify outliers. It is also seen that the proposed robust kernel CCA method performs better for ideal and contaminated data than the standard kernel CCA.