Extended nearest shrunken centroid classification: A new method for open-set authorship attribution of texts of varying sizes

@article{Schaalje2011ExtendedNS,
  title={Extended nearest shrunken centroid classification: A new method for open-set authorship attribution of texts of varying sizes},
  author={G. Bruce Schaalje and Paul J. Fields and Matthew Roper and Gregory L. Snow},
  journal={Lit. Linguistic Comput.},
  year={2011},
  volume={26},
  pages={71-88}
}
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, naı̈ve… 
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References

SHOWING 1-10 OF 37 REFERENCES
Reassessing authorship of the Book of Mormon using delta and nearest shrunken centroid classification
TLDR
The findings support the hypothesis that Rigdon was the main architect of the Book of Mormon and are consistent with historical evidence suggesting that he fabricated the book by adding theology to the unpublished writings of Spalding (then deceased).
A comparative study of machine learning methods for authorship attribution
TLDR
Each of the methods tested performed well, but nearest shrunken centroids and regularized discriminant analysis had the best overall performances with 0/70 cross-validation errors.
Open-Set Nearest Shrunken Centroid Classification
Nearest Shrunken Centroid (NSC) classification has proven successful in ultra-high-dimensional classification problems involving thousands of features measured on relatively few individuals, such as
Testing Authorship in the Personal Writings of Joseph Smith Using NSC Classification
TLDR
The work presented here reevaluates the decision to exclude Joseph Smith and employs both supervised classification and unsupervised clustering in order to explore the stylistic consistency between documents attributed to Smith (but written in the handwriting of Smith's 24 different scribes) and documents in Smith's own hand.
Computational methods in authorship attribution
TLDR
Three scenarios are considered here for which solutions to the basic attribution problem are inadequate; it is shown how machine learning methods can be adapted to handle the special challenges of that variant.
Authorship Attribution
  • P. Juola
  • Art
    Found. Trends Inf. Retr.
  • 2006
TLDR
This review shows that the authorship attribution discipline is quite successful, even in difficult cases involving small documents in unfamiliar and less studied languages; it further analyzes the types of analysis and features used and tries to determine characteristics of well-performing systems, finally formulating these in a set of recommendations for best practices.
'Delta': a Measure of Stylistic Difference and a Guide to Likely Authorship
TLDR
A new way of using the relative frequencies of the very common words for comparing written texts and testing their likely authorship, which offers a simple but comparatively accurate addition to current methods of distinguishing the most likely author of texts exceeding about 1,500 words in length.
Testing Burrows's Delta
  • D. Hoover
  • Linguistics
    Lit. Linguistic Comput.
  • 2004
TLDR
Test of Delta's effectiveness and accuracy shows that it works nearly as well on prose as it does on poetry, and suggests that combining several texts for each author in the primary set reduces the effect of intra-author variability.
Bayesian Analysis of a Multinomial Sequence and Homogeneity of Literary Style
To help settle the debate around the authorship of Tirant lo Blanc, all words in each chapter are categorized according to their length, and the appearances of certain words are counted, thus forming
Classification of microarrays to nearest centroids
TLDR
It is shown that the modified t-statistics and shrunken centroids employed by PAM tend to increase misclassification error when compared with their simpler counterparts, and a classification method called 'Classification to Nearest Centroids' (ClaNC), which is arguably simpler and easier to interpret than PAM.
...
1
2
3
4
...