do.spufs | R Documentation |

This unsupervised feature selection method is based on self-expression model, which means that the cost function involves difference in self-representation. It does not explicitly require learning the clusterings and different features are weighted individually based on their relative importance. The cost function involves two penalties, sparsity and preservation of local structure.

do.spufs( X, ndim = 2, preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate"), alpha = 1, beta = 1, bandwidth = 1 )

`X` |
an |

`ndim` |
an integer-valued target dimension. |

`preprocess` |
an additional option for preprocessing the data.
Default is "null". See also |

`alpha` |
nonnegative number to control sparsity in rows of matrix of representation coefficients. |

`beta` |
nonnegative number to control the degree of local-structure preservation. |

`bandwidth` |
positive number for Gaussian kernel bandwidth to define similarity. |

a named list containing

- Y
an

*(n\times ndim)*matrix whose rows are embedded observations.- featidx
a length-

*ndim*vector of indices with highest scores.- trfinfo
a list containing information for out-of-sample prediction.

- projection
a

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

Kisung You

lu_structure_2018Rdimtools

## 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]) #### try different bandwidth values out1 = do.spufs(X, bandwidth=0.1) out2 = do.spufs(X, bandwidth=1) out3 = do.spufs(X, bandwidth=10) #### visualize opar <- par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(out1$Y, pch=19, col=label, main="SPUFS::bandwidth=0.1") plot(out2$Y, pch=19, col=label, main="SPUFS::bandwidth=1") plot(out3$Y, pch=19, col=label, main="SPUFS::bandwidth=10") par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.

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