Support vector machines for class representation and discrimination

Distinguishing one object class from others is the main task of many classification systems. However, often a classifier must also be able to reject non-object inputs and must thus achieve both rejection and classification. We address this problem with a novel support vector representation and discrimination machine (SVRDM). The support-vector-based nature allows the SVRDM to exhibit good generalization. The SVRDM allows rejection of non-object data, while the standard SVMs do not do well at this. We present results on synthetic data and on the pose, illumination and expression (PIE) database that demonstrate that the SVRDM outperforms popular classifiers.