Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition

332 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Matching Forensic Sketches to Mug Shot Photos (2011, methodology): “Other discriminant analysis methods have been proposed to handle the SSS problem, such as random sampling LDA [23], regularized LDA [24], and direct LDA [25].”
Linear discriminant analysis: A detailed tutorial (2017, unknown): “The third approach, a well-known solution, is to use the regularization method to solve a singular linear systems [38,57].”
Linear vs. quadratic discriminant analysis classifier: a tutorial (2016, methodology): “ηI = + However, choosing the value of the regularisation parameter requires more tuning and a poor choice for this parameter can degrade the performance of the method (Lu et al., 2005).”
Eigenfeature Regularization and Extraction in Face Recognition (2008, methodology): “A modified LDA approach [27] estimates the discriminant value in the full space of S t by replacing S w with S t .”
Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting (2010, methodology): “Recently, the regularization technique has been adopted to tackle small-sample problems in various applications such as face recognition [11]–[13]”
Heterogeneous Face Recognition Using Kernel Prototype Similarities (2013, methodology): “Gentle AdaBoost feature selection was used in conjunction with R-LDA to improve the recognition accuracy.”