odc.clust: Optimal Discriminant Clustering

In SODC: Optimal Discriminant Clustering(ODC) and Sparse Optimal Discriminant Clustering(SODC)

Description

To perform Optimal Discriminant Clustering

Usage

odc.clust(x, centers, cv.num = 5, l2 = -1, clus = kmeans, l2.idx = seq(-3, 3, by = 6/20))

Arguments

x	A numberic dataset matrix.
centers	An integer scalar with the desired number of groups.
cv.num	The numbers of cross validation iteration for selecting tuning parameter lambda2.
l2	L2 penalty parameter, if l2=-1, then odc.clust choose the optimal lambda2 automatically given lambda2.idx. Otherwise perfoms ODC clustering using given lambda2.
clus	The clustering method applied on ODC component.
l2.idx	A sequence of index numbers from which one can get a sequence of lambda2 values by calculating 10^l2.idx.

Value

res	An object of class "kmeans" or other class depending on the value of argument "clus".
opt.lambda2	The optimal lambda2 selected when setting argument l2=-1. This value Will not be returned if setting l2!= -1.

References

Zhang, Z. and Dai, G. (2009). Optimal scoring for unsupervised learning, Advances in Neural Information Processing Systems 23 12: 2241-2249.

SODC documentation built on May 2, 2019, 3:35 p.m.