matrixcomplete: MM Algorithm - Matrix Completion

In gettingtothebottom: Learning Optimization and Machine Learning for Statistics

Description

matrixcomplete Function for performing matrix completion using a majorization-minimization algorithm given data matrix X

Usage

matrixcomplete(X, Z, omega, lambda, maxiter = 100, tol = 1e-04,
  liveupdates = TRUE)

Arguments

X	Data matrix to be completed
Z	Matrix containing last iterates
omega	Vector containing indices of unobserved entries
lambda	Softhreshold parameter
maxiter	(Optional) Max number of iterations (Default: 100)
tol	(Optional) Tolerance for convergence (Default: 1e-4)
liveupdates	(Optional) If FALSE, no notification will be given upon completion of each iteration. (Default: TRUE)

Author(s)

Jocelyn T. Chi

Examples

# (Examples not run)
# Generate an m-by-n test matrix of rank r
# seed <- 12345
# m <- 1000
# n <- 1000
# r <- 5
# T <- testmatrix(m,n,r,seed=seed)

# Add some noise to the test matrix
# E <- 0.1*matrix(rnorm(m*n),m,n)
# A <- T + E

# Obtain a vector of unobserved entries
# temp <- makeOmega(m,n,percent=0.5)
# omega <- temp$omega

# Remove unobserved entries from test matrix
# X <- A
# X[omega] <- NA

# Make initial model matrix Z and find initial lambda
# Z <- matrix(0,m,n)
# lambda <- init.lambda(X,omega)

# Example (Not run)
# Sol <- matrixcomplete(X,Z,omega,lambda)

Example output

Loading required package: ggplot2
Loading required package: grid
Loading required package: Matrix

gettingtothebottom documentation built on May 29, 2017, 8:28 p.m.