#' Parameter-Free Locality Preserving Projection
#'
#' Conventional LPP is known to suffer from sensitivity upon choice of parameters, especially
#' in building neighborhood information. Parameter-Free LPP (PFLPP) takes an alternative step
#' to use normalized Pearson correlation, taking an average of such similarity as a threshold
#' to decide which points are neighbors of a given datum.
#'
#' @param X an \eqn{(n\times p)} matrix or data frame whose rows are observations
#' @param ndim an integer-valued target dimension.
#' @param preprocess an additional option for preprocessing the data.
#' Default is "center". See also \code{\link{aux.preprocess}} for more details.
#'
#' @return a named list containing
#' \describe{
#' \item{Y}{an \eqn{(n\times ndim)} matrix whose rows are embedded observations.}
#' \item{projection}{a \eqn{(p\times ndim)} whose columns are basis for projection.}
#' \item{trfinfo}{a list containing information for out-of-sample prediction.}
#' }
#'
#' @examples
#' ## use iris data
#' data(iris)
#' set.seed(100)
#' subid = sample(1:150, 50)
#' X = as.matrix(iris[subid,1:4])
#' label = as.factor(iris[subid,5])
#'
#' ## compare with PCA
#' out1 = do.pca(X, ndim=2)
#' out2 = do.pflpp(X, ndim=2)
#'
#' ## visualize
#' opar <- par(no.readonly=TRUE)
#' par(mfrow=c(1,2))
#' plot(out1$Y, pch=19, col=label, main="PCA")
#' plot(out2$Y, pch=19, col=label, main="Parameter-Free LPP")
#' par(opar)
#'
#' @references
#' \insertRef{dornaika_enhanced_2013}{Rdimtools}
#'
#' @author Kisung You
#' @rdname linear_PFLPP
#' @concept linear_methods
#' @export
do.pflpp <- function(X, ndim=2, preprocess=c("center","scale","cscale","whiten","decorrelate")){
#------------------------------------------------------------------------
## PREPROCESSING
# 1. data matrix
aux.typecheck(X)
n = nrow(X)
p = ncol(X)
# 2. ndim
ndim = as.integer(ndim)
if (!check_ndim(ndim,p)){stop("* do.pflpp : 'ndim' is a positive integer in [1,#(covariates)).")}
# 3. preprocess
if (missing(preprocess)){
algpreprocess = "center"
} else {
algpreprocess = match.arg(preprocess)
}
#------------------------------------------------------------------------
## COMPUTATION : PRELIMINARY
# 1. preprocessing
tmplist = (X,type=algpreprocess,algtype="linear")
trfinfo = tmplist$info
pX = tmplist$pX
# 2. pearson correlation among samples
tmpP = stats::cor(t(pX)); mintmpP = as.double(min(tmpP))
# 3. normalized correlation
P = (tmpP-mintmpP)/(1-mintmpP)
# 4. mean for each datum
vecm = rowMeans(P)
#------------------------------------------------------------------------
## COMPUTATION : MAIN PART FOR PFLPP
# 1. build adjacency
A = array(0,c(n,n))
for (i in 1:(n-1)){
for (j in (i+1):n){
if (P[i,j] > max(vecm[i],vecm[j])){
A[i,j] = P[i,j]
A[j,i] = P[i,j]
}
}
}
# 2. build for L
D = diag(rowSums(A))
L = D-A
# 3. cost function with respect to geigen problem
LHS = t(pX)%*%L%*%pX
RHS = t(pX)%*%D%*%pX
# 4. projection vectors
projection = aux.geigen(LHS, RHS, ndim, maximal=FALSE)
#------------------------------------------------------------------------
## RETURN
result = list()
result$Y = pX%*%projection
result$trfinfo = trfinfo
result$projection = projection
return(result)
}
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