TY - GEN
T1 - Laplacian margin distribution boosting for learning from sparsely labeled data
AU - Wang, Tao
AU - He, Xuming
AU - Shen, Chunhua
AU - Barnes, Nick
PY - 2011
Y1 - 2011
N2 - Boosting algorithms attract much attention in computer vision and image processing because of their strong performance in a variety of applications. Recent progress on the theory of boosting algorithms suggests a close link between good generalization and the margin distrubtion of the classifier \wrt a dataset. In this paper, we propose a novel data-dependent margin distribution learning criterion for boosting, termed Laplacian MDBoost, which utilizes the intrinsic geometric structure of dataset. One key aspect of our method is that it can seamlessly incorporate unlabeled data by including a graph Laplacian regularizer. We derive a dual formulation of the learning problem that can be efficiently solved by column generation. Experiments on various datasets validate the effectiveness of the new graph Laplacian based learning criterion on both supervised and unsupervised learning settings. We also show that the performance of our algorithm outperforms the state-of-the-art semi-supervised learning algorithms on a variety of inductive inference tasks, including real world video segmentation.
AB - Boosting algorithms attract much attention in computer vision and image processing because of their strong performance in a variety of applications. Recent progress on the theory of boosting algorithms suggests a close link between good generalization and the margin distrubtion of the classifier \wrt a dataset. In this paper, we propose a novel data-dependent margin distribution learning criterion for boosting, termed Laplacian MDBoost, which utilizes the intrinsic geometric structure of dataset. One key aspect of our method is that it can seamlessly incorporate unlabeled data by including a graph Laplacian regularizer. We derive a dual formulation of the learning problem that can be efficiently solved by column generation. Experiments on various datasets validate the effectiveness of the new graph Laplacian based learning criterion on both supervised and unsupervised learning settings. We also show that the performance of our algorithm outperforms the state-of-the-art semi-supervised learning algorithms on a variety of inductive inference tasks, including real world video segmentation.
KW - Boosting algorithms
KW - graph Laplacian
KW - margin distribution
KW - semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=84863048230&partnerID=8YFLogxK
U2 - 10.1109/DICTA.2011.42
DO - 10.1109/DICTA.2011.42
M3 - Conference contribution
SN - 9780769545882
T3 - Proceedings - 2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011
SP - 209
EP - 216
BT - Proceedings - 2011 International Conference on Digital Image Computing
T2 - 2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011
Y2 - 6 December 2011 through 8 December 2011
ER -