An outlier map for Support Vector Machine classification

TL;DR

This paper introduces an outlier map for SVM classification that visualizes data and identifies outliers in high-dimensional kernel spaces, enhancing data quality assessment.

Contribution

It extends the Stahel-Donoho outlyingness measure to kernel spaces and proposes a trimmed SVM for outlier detection and visualization.

Findings

Effective visualization of outliers in high-dimensional data

Application to biological datasets demonstrates practical utility

Improved data quality assessment in SVM classification

Abstract

Support Vector Machines are a widely used classification technique. They are computationally efficient and provide excellent predictions even for high-dimensional data. Moreover, Support Vector Machines are very flexible due to the incorporation of kernel functions. The latter allow to model nonlinearity, but also to deal with nonnumerical data such as protein strings. However, Support Vector Machines can suffer a lot from unclean data containing, for example, outliers or mislabeled observations. Although several outlier detection schemes have been proposed in the literature, the selection of outliers versus nonoutliers is often rather ad hoc and does not provide much insight in the data. In robust multivariate statistics outlier maps are quite popular tools to assess the quality of data under consideration. They provide a visual representation of the data depicting several types of outliers. This paper proposes an outlier map designed for Support Vector Machine classification. The Stahel--Donoho outlyingness measure from multivariate statistics is extended to an arbitrary kernel space. A trimmed version of Support Vector Machines is defined trimming part of the samples with largest outlyingness. Based on this classifier, an outlier map is constructed visualizing data in any type of high-dimensional kernel space. The outlier map is illustrated on 4 biological examples showing its use in exploratory data analysis.