Description Usage Arguments Value
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
.
1 2 3 |
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 |
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 |
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")
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