# LRR: Low-Rank Representation In T4cluster: Tools for Cluster Analysis

## Description

Low-Rank Representation (LRR) constructs the connectivity of the data by solving

for column-stacked data matrix D and \|\cdot \|_* is the nuclear norm which is relaxation of the rank condition. If you are interested in full implementation of the algorithm with sparse outliers and noise, please contact the maintainer.

## Usage

 1 LRR(data, k = 2, rank = 2) 

## Arguments

 data an (n\times p) matrix of row-stacked observations. k the number of clusters (default: 2). rank sum of dimensions for all k subspaces (default: 2).

## Value

a named list of S3 class T4cluster containing

cluster

a length-n vector of class labels (from 1:k).

algorithm

name of the algorithm.

## References

\insertRef

liu_robust_2010T4cluster

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 ## generate a toy example set.seed(10) tester = genLP(n=100, nl=2, np=1, iso.var=0.1) data = tester$data label = tester$class ## do PCA for data reduction proj = base::eigen(stats::cov(data))$vectors[,1:2] dat2 = data%*%proj ## run LRR algorithm with k=2, 3, and 4 with rank=4 output2 = LRR(data, k=2, rank=4) output3 = LRR(data, k=3, rank=4) output4 = LRR(data, k=4, rank=4) ## extract label information lab2 = output2$cluster lab3 = output3$cluster lab4 = output4$cluster ## visualize opar <- par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(dat2, pch=19, cex=0.9, col=lab2, main="LRR:K=2") plot(dat2, pch=19, cex=0.9, col=lab3, main="LRR:K=3") plot(dat2, pch=19, cex=0.9, col=lab4, main="LRR:K=4") par(opar) 

T4cluster documentation built on Aug. 16, 2021, 9:07 a.m.