Regularized semi-supervised classification on manifold

Authors:
Lianwei Zhao;Siwei Luo;Yanchang Zhao;Lingzhi Liao;Zhihai Wang
Affiliations:
School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;Faculty of Information Technology, University of Technology, Sydney, Australia;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

Citing 5
Cited 0

Combining labeled and unlabeled data with co-training

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning from Labeled and Unlabeled Data using Graph Mincuts

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
Learning from Examples as an Inverse Problem

The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Semi-supervised learning gets estimated marginal distribution PX with a large number of unlabeled examples and then constrains the conditional probability p(y | x) with a few labeled examples. In this paper, we focus on a regularization approach for semi-supervised classification. The label information graph is first defined to keep the pairwise label relationship and can be incorporated with neighborhood graph which reflects the intrinsic geometry structure of PX. Then we propose a novel regularized semi-supervised classification algorithm, in which the regularization term is based on the modified Graph Laplacian. By redefining the Graph Laplacian, we can adjust and optimize the decision boundary using the labeled examples. The new algorithm combines the benefits of both unsupervised and supervised learning and can use unlabeled and labeled examples effectively. Encouraging experimental results are presented on both synthetic and real world datasets.