  Multilabel classification is an important problem in a wide range of domains
such as text categorization and music annotation. In this paper, we present a
probabilistic model, Multilabel Logistic Regression with Hidden variables
(MLRH), which extends the standard logistic regression by introducing hidden
variables. Hidden variables make it possible to go beyond the conventional
multiclass logistic regression by relaxing the one-hot-encoding constraint. We
define a new joint distribution of labels and hidden variables which enables us
to obtain one classifier for multilabel classification. Our experimental
studies on a set of benchmark datasets demonstrate that the probabilistic model
can achieve competitive performance compared with other multilabel learning
algorithms.
