ICML 2011 workshop on unsupervised
and transfer learning
Transfer
Learning with Cluster Ensembles
Ayan
Acharya1
Eduardo R. Hruschka1,2
Joydeep Ghosh1
Sreangsu Acharyya1
1University of Texas (UT) at Austin, USA
2University of Sao Paulo (USP) at Sao Carlos, Brazil
Traditional supervised learning algorithms are usually unsuitable for
transfer learning because they assume that the training and
test/scoring data come from a common underlying distribution. This
problem is exacerbated when the "test" data actually represents a
related but different task but there are no labeled examples in the
test set to help us readily discern what changes may have come about.
We introduce a general optimization framework that takes as input one
or more classifiers learned on the original task as well as the results
of a cluster ensemble operating solely on the target task data, and
yields a consensus labeling of the target data. This framework is
general in that it admits a wide range of loss functions and
classification/clustering methods. Empirical results on both text and
hyperspectral data indicate that the proposed method can yield
substantially superior classification results as compared to applying
certain other transductive learning techniques or naively applying the
classifier (ensembles) learnt on the original task to the target data.