Low-Rank Representation (LRR) constructs the connectivity of the data by solving $$\textrm{min}_C \|C\|_*\quad\textrm{such that}\quad D=DC$$ 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.

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

## Arguments

data an $$(n\times p)$$ matrix of row-stacked observations. the number of clusters (default: 2). 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

Liu G, Lin Z, Yu Y (2010). “Robust Subspace Segmentation by Low-Rank Representation.” In Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML'10, 663--670. ISBN 978-1-60558-907-7.

## Examples

# \donttest{
## 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