Extended nearest shrunken centroid classification: A new method for open-set authorship attribution of texts of varying sizes
G. Bruce Schaalje, Paul J. Fields, Marc Roper, Gregory L. Snow · Literary and Linguistic Computing · 2011
The nearest shrunken centroid (NSC) methodology, originally developed for high-dimensional genomics problems, was recently applied in a stylometric study. Although NSC has many advantages, stylometric problems usually differ from genomics problems in several important ways: texts are of a wide range of sizes, a large series of texts are often the subjects for classification, and most importantly the set of candidate authors cannot usually be assumed to be closed. Consequently, naïve application of NSC methodology can produce misleading results. We extend the NSC methodology for more general application to stylometry. Reanalysis of the Book of Mormon using the open-set NSC method produced dramatically different results from a closed-set NSC analysis.