scvxclust: Sparse Convex Clustering Path

In elong0527/scvxclustr: Sparse convex clustering

Description

The function estimates the sparse convex clustering path via AMA or ADMM. Required inputs include a data matrix X (rows are samples; columns are features), a vector of weights w, regularization parameters Gamma1, Gamma2 and the adaptive weight Gamma2_weight.

Usage

scvxclust(X, w, Gamma1, Gamma2, Gamma2_weight, nu = 1, tol_abs = 0.001,
  tol_rel = 1e-04, max_iter = 10000, type = 2, verbose = F,
  method = "ama", init = NULL)

Arguments

X	The data matrix to be clustered. The rows are the samples, and the columns are the features.
w	A vector of nonnegative weights. The ith entry w[i] denotes the weight used between the ith pair of centroids. The weights are in dictionary order.
Gamma1	A regularization parameter controls cluster size .
Gamma2	A regularization parameter controls the number of informative features .
Gamma2_weight	The weight to adaptively penalize the features.
nu	A positive penalty parameter for quadratic deviation term.
tol_abs	The convergence tolerance (absolute).
tol_rel	The convergence tolerance (relative).
max_iter	The maximum number of iterations.
type	An integer indicating the norm used: 2 = 2-norm. (Only L2 norm are supported for now)
verbose	report convergence information
method	method to fit the sparse convex clustering ("ama" or "admm"). Default is ama
init	initial vlaue of the method

Value

U A list of centroid matrices.

V A list of centroid difference matrices.

Lambda A list of Lagrange multiplier matrices.

iters number of iterations.

eva the absolute difference of U between two most recent iteration.

method fitted method ("ama" or "admm")

elong0527/scvxclustr documentation built on May 16, 2019, 3:56 a.m.