robustcvxclust_path: Robust Convex Clustering

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

Robust Convex Clustering

Description

robustcvxclust_path estimates the robust convex clustering path using ADMM. Required inputs include a data matrix X (rows are the observations; columns are the features), a vector of weights wt, and a sequence of regularization parameters lambda. The penalty norm supported is 2-norm.

Usage

robustcvxclust_path(X, wt, lambda, rho, tau, tol_abs = 1e-05, max_iter = 1e+05)

Arguments

X	The data matrix to be clustered. The rows are the observations, and the columns are the features.
wt	A vector of nonnegative weights. The ith entry w[i] denotes the weight used between the ith pair of centroids.
lambda	A sequence of regularization parameters.
rho	Augmented Lagrangian penalty parameter.
tau	The robustification parameter in huber loss.
tol_abs	The convergence tolerance (absolute).
max_iter	The maximum number of iterations.

Value

U A list of centroid matrices.

W A list of centroid matrices.

V A list of centroid difference matrices.

Y A list of Lagrange multiplier matrices.

Z A list of Lagrange multiplier matrices.

Author(s)

Chenyu Liu, Qiang Sun, Kean Ming Tan

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