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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