PP.optimize: Find optimal projection by maximizing selected PP index

In EK-Lee/classPP: Projection Pursuit for supervised classification

Description

Find optimal projection using PP index.

Usage

PP.optimize.random(PPmethod, projdim, data, class, std=TRUE,
                   cooling=0.99, temp=1, r=NULL, lambda=NULL, weight=TRUE, ...)
PP.optimize.anneal(PPmethod, projdim, data, class, std=TRUE,
                   cooling=0.999, temp=1, energy=0.01,
                   r=NULL, lambda=NULL, weight=TRUE, ...)
PP.optimize.Huber(PPmethod, projdim, data, class, std=TRUE,
                  cooling=0.99, temp=1, r=NULL, lambda=NULL,
                  weight=TRUE, ...)
PP.optimize.plot(PP.opt, data, class, std=TRUE)

Arguments

PPmethod	Selected PP index “LDA" - LDA index “Lp" - Lp index; “PDA" - PDA index
projdim	dimension of projection that you want to find
data	data without class information
class	class information
std	decide whether data will be standardized or not before applying projection pursuit
weight	weight flag using in LDA index
cooling	parameter for optimization
temp	inital temperature for optimization
energy	parameter for simulated annealing optimization
r	a parameter for L_r index
lambda	a parameter for PDA index
PP.opt	the optimal projection
...	...

Value

index.best	PP index for optimal projected data
proj.best	optimal projection

Author(s)

Eun-kyung Lee

References

Lee E., Cook D., and Klinke, S. (2002) Projection Pursuit indices for supervised classification

See Also

{PPindex.class}

Examples

data(iris)

PP.opt<-PP.optimize.random("LDA",1,iris[,1:4],iris[,5],cooling=0.999,temp=1)

PP.opt$index.best
PP.optimize.plot(PP.opt,iris[,1:4],iris[,5])


PP.opt<-PP.optimize.anneal("LDA",1,iris[,1:4],iris[,5],cooling=0.999,temp=1,energy=0.01)
PP.opt$index.best

PP.optimize.plot(PP.opt,iris[,1:4],iris[,5])


PP.opt<-PP.optimize.Huber("LDA",2,iris[,1:4],iris[,5],cooling=0.999,r=1)
PP.opt$index.best
PP.optimize.plot(PP.opt,iris[,1:4],iris[,5])

EK-Lee/classPP documentation built on May 6, 2019, 3:09 p.m.