Single Class Discrimination Using Principal Component Analysis (SCD‐PCA)

Abstract Single Class Discrimination using Principal Component Analysis (SCD‐PCA) has been developed to discriminate an embedded data class. The embedded class is defined as the active class and the diffuse class, or classes as the inactives. Two basic methods are described. Both involve an initial column centring and scaling of the data to the actives space. Method I then directly obtains the directions of maximum variance in the inactives by PCA, while method II attempts to maximize the ratio of inactives to actives variance using PCA. The methods offer good potential to reveal a tightly bedded active class near the origin with the inactives dispersed. Techniques to portray the discrimination power and significance of the PCs are discussed. SCD‐PCA has been applied to artificial data sets exhibiting a variety of distributional characteristics and to two QSAR data sets. The method performed predictably on the artificial data. Good, low dimensional PC models were obtained for the QSAR data sets which were stable when cross‐validated.