Countering the false positive projection effect in nonlinear asymmetric classification

This work concerns the problem of asymmetric classification and provides the following contributions. First, it introduces the method of KDDA - a kernelized extension of the distance-based discriminant analysis technique that treats data asymmetrically and naturally accommodates indefinite kernels. Second, it demonstrates that KDDA and other asymmetric nonlinear projective approaches, such as BiasMap and KFD are often prone to an adverse condition referred to as the false positive projection effect. Empirical evaluation on both synthetic and real-world data sets is carried out to assess the degree of performance degradation due to false positive projection effect, determine the viability of some schemes for its elimination, and compare the introduced KDDA method with state-of-the-art alternatives, achieving encouraging results