# feature_FOSMOD: Forward Orthogonal Search by Maximizing the Overall... In Rdimtools: Dimension Reduction and Estimation Methods

 do.fosmod R Documentation

## Forward Orthogonal Search by Maximizing the Overall Dependency

### Description

The FOS-MOD algorithm \insertCitewei_2007_FeatureSubsetSelectionRdimtools is an unsupervised algorithm that selects a desired number of features in a forward manner by ranking the features using the squared correlation coefficient and sequential orthogonalization.

### Usage

do.fosmod(X, ndim = 2, ...)


### Arguments

 X an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables. ndim an integer-valued target dimension (default: 2). ... extra parameters including preprocessan additional option for preprocessing the data. See also aux.preprocess for more details (default: "center").

### Value

a named Rdimtools S3 object containing

Y

an (n\times ndim) matrix whose rows are embedded observations.

featidx

a length-ndim vector of indices with highest scores.

projection

a (p\times ndim) whose columns are basis for projection.

trfinfo

a list containing information for out-of-sample prediction.

algorithm

name of the algorithm.

\insertAllCited

### Examples


## 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 with other methods
out1 = do.fosmod(iris.dat)
out2 = do.lscore(iris.dat)
out3 = do.fscore(iris.dat, iris.lab)

## visualize
plot(out1$Y, pch=19, col=iris.lab, main="FOS-MOD") plot(out2$Y, pch=19, col=iris.lab, main="Laplacian Score")