robustcvxclust: Robust Convex Clustering

In JhZhang-1999/Rcvxclustr: Robust convex clustering: How does the fusion penalty enhance robustness?

Robust Convex Clustering

Description

robustcvxclust performs robust convex clustering using ADMM. This is an R wrapper function around C code. Dimensions of various arguments are as follows:

• n is the number of data observations

• p is the number of features

• nK is the number non-zero weights.

Usage

robustcvxclust(
  X,
  W = NULL,
  V = NULL,
  Y = NULL,
  Z = NULL,
  max_iter = 1e+05,
  rho,
  tau,
  lambda,
  wt,
  tol_abs = 1e-05
)

Arguments

X	The n-by-p data matrix whose rows are being clustered.
W	The n-by-p matrix of Lagrange multipliers.
V	The centroid difference matrix.
Y	The nK-by-p matrix of Lagrange multipliers.
Z	The n-by-p matrix of Lagrange multipliers.
max_iter	The maximum number of iterations. The default value is 1e5.
rho	Augmented Lagrangian penalty parameter.
tau	The robustification parameter in huber loss.
lambda	The regularization parameter controlling the amount of shrinkage.
wt	A vector of nK positive weights.
tol_abs	The convergence tolerance. The default value is 1e-05.

Value

U The centroid matrix.

W The centroid matrix.

V The centroid difference matrix.

Y The Lagrange multiplier matrix.

Z The Lagrange multiplier matrix.

iter The number of iterations taken.

tol The residual tolerances.

Author(s)

Chenyu Liu, Qiang Sun, Kean Ming Tan

JhZhang-1999/Rcvxclustr documentation built on Aug. 18, 2022, 9:25 a.m.