Locality Pursuit Embedding (LPE) is an unsupervised linear dimension reduction method. It aims at preserving local structure by solving a variational problem that models the local geometrical structure by the Euclidean distances.
an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.
an integer-valued target dimension.
an additional option for preprocessing the data.
Default is "center". See also aux.preprocess
for more details.
size of \(k\)-nn neighborhood in original dimensional space.
a named list containing
an \((n\times ndim)\) matrix whose rows are embedded observations.
a list containing information for out-of-sample prediction.
a \((p\times ndim)\) whose columns are basis for projection.
Min W, Lu K, He X (2004). “Locality Pursuit Embedding.” Pattern Recognition, 37(4), 781--788.
# \donttest{
## generate swiss roll with auxiliary dimensions
set.seed(100)
n = 100
theta = runif(n)
h = runif(n)
t = (1+2*theta)*(3*pi/2)
X = array(0,c(n,10))
X[,1] = t*cos(t)
X[,2] = 21*h
X[,3] = t*sin(t)
X[,4:10] = matrix(runif(7*n), nrow=n)
## try with different neighborhood sizes
out1 = do.lpe(X, numk=5)
out2 = do.lpe(X, numk=10)
out3 = do.lpe(X, numk=25)
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, main="LPE::numk=5")
plot(out2$Y, main="LPE::numk=10")
plot(out3$Y, main="LPE::numk=25")
par(opar)
# }