robpca: robpca

In RBigData/pbdML: Programming with Big Data -- Machine Learning

Description

The optimization problem is solved by an alternating directions technique.

Usage

robpca(M, delta = 1e-07, maxiter = 1000)

Arguments

M	The input data, stored as a numeric matrix or ddmatrix.
delta	Numeric termination criteria. A smaller (closer to 0) value will require more iterations. See the summary following the Algorithm 1 listing in the referenced paper for details.
maxiter	Maximum number of iterations. Should at least be a few hundred.

Details

Implementation of the robust pca algorithm.

Author(s)

Drew Schmidt

References

Candes, E.J., Li, X., Ma, Y. and Wright, J., 2011. Robust principal component analysis?. Journal of the ACM (JACM), 58(3), p.11.

Examples

## Not run: 
m = 10
n = 3
M = matrix(rnorm(m*n), m)
robsvd(M)

## End(Not run)

RBigData/pbdML documentation built on July 12, 2019, 6:12 p.m.