sslMincut implements the Mincut algorithm for maxflow graph partition in the k-nearest neighbor graph.
Usage
sslMincut(xl, yl, xu, simil.type = "correlation", k = 10)
Arguments
xl
a n * p matrix or data.frame of labeled data
yl
a n * 1 binary labels(1 or -1).
xu
a m * p matrix or data.frame of unlabeled data.
simil.type
character string; this parameter controls the type of similarity measurement.(see simil or pr_DB).
k
an integer parameter controls a k-nearest neighbor graph.
Value
a m * 1 integer vector representing the predicted labels of unlabeled data.
Details
sslMincut creates a k-nearest neighbor graph and finds a maxflow
from the first postive observation to the first negative one based on MPLA algorithm. This
maxflow partitions the graph into postive labels and negative ones.
References
Blum, A., & Chawla, S. (2001). Learning from labeled and unlabeled data using
graph mincuts. Proc. 18th International Conf. on Machine Learning.