Unsupervised classification via convex absolute value inequalities

Abstract

We consider the problem of classifying completely unlabelled data using convex inequalities that contain absolute values of the data. This allows each data point to belong to either one of two classes by entering the inequality with a plus or minus value. Using such absolute value inequalities in support vector machine classifiers, unlabelled data can be successfully partitioned into two classes that capture most of the correct labels dropped from the data. Inclusion of partially labelled data leads to a semisupervised classifier. Computational results include unsupervised and semisupervised classification of the Wisconsin Breast Cancer Wisconsin (Diagnostic) Data Set.

Keywords:

• unsupervised classification
• absolute value inequalities
• support vector machines

AMS Subject Classifications:

• 52A40 Inequalities and Extremum Problems

Acknowledgements

The research described here is based on Data Mining Institute Report 14-01, March 2014.