do.mlie | R Documentation |
Maximal Local Interclass Embedding (MLIE) is a linear supervised method that the local interclass graph and the intrinsic graph are constructed to find a set of projections that maximize the local interclass scatter and the local intraclass compactness at the same time. It can be deemed an extended version of MFA.
do.mlie( X, label, ndim = 2, preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"), k1 = max(ceiling(nrow(X)/10), 2), k2 = max(ceiling(nrow(X)/10), 2) )
X |
an (n\times p) matrix or data frame whose rows are observations. |
label |
a length-n vector of data class labels. |
ndim |
an integer-valued target dimension. |
preprocess |
an additional option for preprocessing the data.
Default is "center". See also |
k1 |
the number of same-class neighboring points (homogeneous neighbors). |
k2 |
the number of different-class neighboring points (heterogeneous neighbors). |
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.
lai_maximal_2011Rdimtools
do.mfa
## Not run: ## generate data of 3 types with clear difference set.seed(100) diff = 100 dt1 = aux.gensamples(n=20)-diff dt2 = aux.gensamples(n=20) dt3 = aux.gensamples(n=20)+diff ## merge the data and create a label correspondingly X = rbind(dt1,dt2,dt3) label = rep(1:3, each=20) ## try different numbers for neighborhood size out1 = do.mlie(X, label, k1=5, k2=5) out2 = do.mlie(X, label, k1=10,k2=10) out3 = do.mlie(X, label, k1=25,k2=25) ## visualize opar <- par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(out1$Y, main="MLIE::nbd size=5") plot(out2$Y, main="MLIE::nbd size=10") plot(out3$Y, main="MLIE::nbd size=25") par(opar) ## End(Not run)
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