A robust support vector machine for labeling errors

ABSTRACT

Support vector machine (SVM) is sparse in that its classifier is expressed as a linear combination of only a few support vectors (SVs). Whenever an outlier is included as an SV in the classifier, the outlier may have serious impact on the estimated decision function. In this article, we propose a robust loss function that is convex. Our learning algorithm is more robust to outliers than SVM. Also the convexity of our loss function permits an efficient solution path algorithm. Through simulated and real data analysis, we illustrate that our method can be useful in the presence of labeling errors.

KEYWORDS:

• Classification
• Convexity
• Loss function
• Outlier
• Solution path algorithm

MATHEMATICS SUBJECT CLASSIFICATION:

• Primary 62H30
• Secondary 68Q32

Additional information

Funding

The research of Hosik Choi was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2007611). The research of Changyi Park was supported by the 2015 sabbatical year research grant of the University of Seoul.