Table 4.

Advantages and disadvantages of using the normal model (NM), optimal scoring (OS), and Fisher’s discriminant analysis (FD) as the basis for penalized LDA with an L₁ penalty

	Advantages	Disadvantages	Citation
NM	Sparse class means if diagonal estimate of Σw used. Computations are fast.	Does not give sparse discriminant vectors. No reduced-rank classification.	Tibshirani et al. (2002)
OS	Sparse discriminant vectors.	Dificult to enforce diagonal estimate for Σw, which is useful if p > n. Computations can be slow.	Grosenick et al. (2008) Leng (2008) Clemmensen et al. (2011)
FD	Sparse discriminant vectors. Simple to en-force diagonal estimate of Σw. Computations are fast using diagonal estimate of Σw.	Computations can be slow when p is large, unless diagonal estimate of Σw is used.	This work.