kernel_weights

The data matrix to be clustered. The rows are the features, and the columns are the samples.

The nonnegative parameter that controls the scale of kernel weights

<code>kernel_weights</code> computes Gaussian kernel weights given a data matrix <code>X</code> and a scale parameter <code>phi</code>. Namely,
the lth weight <code>w[l]</code> is given by
$$w[l] = exp(-phi ||X[,i]-X[,j]||^2)$$, where the lth pair of nodes is (<code>i</code>,<code>j</code>).

Alternating Minimization Algorithm (AMA) and Alternating Direction
Method of Multipliers (ADMM) splitting methods for convex clustering.

kernel_weights: Compute Gaussian Kernel Weights

Description

Usage

Arguments

Value