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#' Locality Sensitive Laplacian Score
#'
#' Locality Sensitive Laplacian Score (LSLS) is a supervised linear feature extraction method that combines
#' a feature selection framework of laplacian score where the graph laplacian is adjusted as in the
#' scheme of LSDA. The adjustment is taken via decomposed affinity matrices which are separately constructed
#' using the provided class label information.
#'
#' @seealso \code{\link{do.lsda}}, \code{\link{do.lscore}}
#'
#' @param X an \eqn{(n\times p)} matrix or data frame whose rows are observations
#' and columns represent independent variables.
#' @param label a length-\eqn{n} vector of data class labels.
#' @param ndim an integer-valued target dimension.
#' @param alpha a weight factor; should be a real number in \eqn{[0,1]}.
#' @param k an integer; the size of a neighborhood.
#' @param preprocess an additional option for preprocessing the data.
#' Default is "null". 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{featidx}{a length-\eqn{ndim} vector of indices with highest scores.}
#' \item{trfinfo}{a list containing information for out-of-sample prediction.}
#' \item{projection}{a \eqn{(p\times ndim)} whose columns are basis for projection.}
#' }
#'
#' @examples
#' \donttest{
#' ## use iris data
#' ## it is known that feature 3 and 4 are more important.
#' data(iris)
#' set.seed(100)
#' subid = sample(1:150,50)
#' iris.dat = as.matrix(iris[subid,1:4])
#' iris.lab = as.factor(iris[subid,5])
#'
#' ## compare different neighborhood sizes
#' out1 = do.lsls(iris.dat, iris.lab, k=3)
#' out2 = do.lsls(iris.dat, iris.lab, k=6)
#' out3 = do.lsls(iris.dat, iris.lab, k=9)
#'
#' ## visualize
#' opar <- par(no.readonly=TRUE)
#' par(mfrow=c(1,3))
#' plot(out1$Y, col=iris.lab, pch=19, main="LSLS::k=3")
#' plot(out2$Y, col=iris.lab, pch=19, main="LSLS::k=6")
#' plot(out3$Y, col=iris.lab, pch=19, main="LSLS::k=9")
#' par(opar)
#' }
#'
#' @references
#' \insertRef{liao_gene_2014a}{Rdimtools}
#'
#' @rdname feature_LSLS
#' @author Kisung You
#' @concept feature_methods
#' @export
do.lsls <- function(X, label, ndim=2, alpha=0.5, k=5, preprocess=c("null","center","scale","cscale","decorrelate","whiten")){
#------------------------------------------------------------------------
## PREPROCESSING
# 1. data matrix
aux.typecheck(X)
n = nrow(X)
p = ncol(X)
# 2. label vector
label = check_label(label, n)
ulabel = unique(label)
C = length(ulabel)
if (C==1){
stop("* do.lsls : 'label' should have at least 2 unique labelings.")
}
if (C==n){
stop("* do.lsls : given 'label' has all unique elements.")
}
if (any(is.na(label))||(any(is.infinite(label)))){
stop("* Supervised Learning : any element of 'label' as NA or Inf will simply be considered as a class, not missing entries.")
}
# 3. ndim
ndim = as.integer(ndim)
if (!check_ndim(ndim,p)){
stop("* do.lsls : 'ndim' is a positive integer in [1,#(covariates)].")
}
# 4. preprocess
if (missing(preprocess)){
algpreprocess = "null"
} else {
algpreprocess = match.arg(preprocess)
}
# 5. alpha (weight parameter in [0,1])
myalpha = as.double(alpha)
if ((length(alpha)>1)||(alpha<0)||(alpha>1)){
stop("* do.lsls : weight factor 'alpha' should be a value in [0,1].")
}
# 6. k (neighborhood size)
myk = round(k)
#------------------------------------------------------------------------
## STEP 1. PREPROCESSING OF THE DATA
tmplist = (X,type=algpreprocess,algtype="linear")
trfinfo = tmplist$info
pX = tmplist$pX
## STEP 2. NEIGHBORHOOD INFORMATION
nbdobj = RANN::nn2(pX, k=(myk+1))
nbdinfo = nbdobj$nn.idx[,2:(myk+1)]
## STEP 3. COMPUTE W, Wb, and Ww
# 3-1. masking
matW = method_lsls(pX, nbdinfo)
vecD = base::rowSums(matW)
# 3-2. separation
matWb = array(0,c(n,n)) # between (different)
matWw = array(0,c(n,n)) # within (same)
for (i in 1:(n-1)){
for (j in (i+1):n){
if (label[i]==label[j]){ # within (same)
matWw[i,j] <- matWw[j,i] <- matW[i,j]
} else {
matWb[i,j] <- matWb[j,i] <- matW[i,j]
}
}
}
## STEP 4. WEIGHTED LAPLACIAN (STILL MYSTERIOUS)
matL = myalpha*(base::diag(base::rowSums(matWw))-matWw) - (1-myalpha)*(base::diag(base::rowSums(matWb))-matWb)
## STEP 5. COMPUTE LSLS SCORES
lscore = rep(0,p)
for (i in 1:p){
vecr = as.vector(pX[,i]) # choose the vector
vecrc = vecr - (base::sum(vecr*vecD)/base::sum(vecD))*rep(1,n) # centering
term1 = base::sum(vecrc*as.vector(matL%*%vecrc))
term2 = base::sum(vecrc*(base::sum(vecrc*vecD)))
lscore[i] = term1/term2
}
## STEP 6. POST-PROCESSING
# 6-1. choose the smallest ones
idxvec = base::order(lscore, decreasing = FALSE)[1:ndim]
# 6-2. projection matrix
projection = aux.featureindicator(p,ndim,idxvec)
#------------------------------------------------------------------------
## RETURN
result = list()
result$Y = pX%*%projection
result$featidx = idxvec
result$trfinfo = trfinfo
result$projection = projection
return(result)
}
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