principal_graph_large: function to automatically learn the structure of data by...

In cole-trapnell-lab/L1-graph: Finding Principal Graphs via L1 Optimization

Description

function to automatically learn the structure of data by either using L1-graph or the spanning-tree formulization

Usage

principal_graph_large(X, y, maxiter = 10, eps = 1e-05,
  gstruct = c("l1-graph", "span-tree"), lambda = 1, gamma = 0.5,
  sigma = 0.01, nn = 5, ncenter = NULL, verbose = T)

Arguments

X	the input data DxN
y	the initial cluster assignment
maxiter	maximum number of iteraction
eps	relative objective difference
gstruct	graph structure to learn, either L1-graph or the span-tree
lambda	regularization parameter for inverse graph embedding
gamma	regularization parameter for k-means (the prefix of 'param' is used to avoid name collision with gamma)
sigma	bandwidth parameter
nn	number of nearest neighbors
verbose	emit results from iteraction
C0	the initialization of centroids
G	graph matrix with side information where cannot-link pair is 0

Value

a list of X, C, W, P, objs X is the input data C is the centers for principal graph W is the pricipal graph matrix P is the cluster assignment matrix objs is the objective value for the function

cole-trapnell-lab/L1-graph documentation built on May 17, 2019, 12:50 p.m.