Using discriminant analysis for multi-class classification: an experimental investigation

By Tao Li, Shenghuo Zhu, Mitsunori Ogihara. Knowledge and Information Systems, 2006

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Linear discriminant analysis: A detailed tutorial (2017, unknown): “The eigenvectors represent the directions of the new space, and the corresponding eigenvalues represent the scaling factor, length, or the magnitude of the eigenvectors [34,59].”
Mining incomplete survey data through classification (2010, methodology): “In this study, we propose to use information of classification on the complete data sub-set as the reference, since a survey usually associates with a problem of classification (or discriminant analysis), and the classification in turn associates with a specific decision making problem [12,16,31,32]”
Top 10 algorithms in data mining (2007, background): “this article are among the most influential algorithms for classification [47,51,77], clustering [11,31,40,44–46], statistical learning [28,76,92], association analysis [2,6,13,50,54,74], and link mining.”