Description Usage Arguments Value
function to automatically learn the structure of data by either using L1-graph or the spanning-tree formulization
1 2 3 | principal_graph(X, C0, G, maxiter = 10, eps = 1e-05,
gstruct = c("l1-graph", "span-tree"), L1.lambda = 1, L1.gamma = 0.5,
L1.sigma = 0.01, nn = 5, L1.timeout = 1800, verbose = T)
|
X |
the input data DxN |
C0 |
the initialization of centroids |
G |
graph matrix with side information where cannot-link pair is 0 |
maxiter |
maximum number of iteraction |
eps |
relative objective difference |
gstruct |
graph structure to learn, either L1-graph or the span-tree |
L1.lambda |
regularization parameter for inverse graph embedding |
L1.gamma |
regularization parameter for k-means (the prefix of 'param' is used to avoid name collision with gamma) |
L1.sigma |
bandwidth parameter |
nn |
number of nearest neighbors |
L1.timeout |
a positive integer value specifying the number of seconds after which a timeout will occur. If zero, then no timeout will occur. (This is a parameter passed to lp.control function) |
verbose |
emit results from iteraction |
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
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