Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Graph-Based Semisupervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
A nonparametric classification method based on K-associated graphs
Information Sciences: an International Journal
| Hi-index | 0.00 |
The increasing on the human ability to gather data has led to an increasing effort on labeling them to be used in specific applications such as classification and regression. Therefore, automatic labeling methods such as semi-supervised transdutive learning algorithms are of a major concern on the machine learning and data mining community nowadays. This paper proposes a graph-based algorithm which uses the purity measure to help spreading the labels throughout the graph. The purity measure determines how intertwined are different subspaces of data regarding its classes. As high values of purity indicate low mixture among patterns of different classes, its maximization helps finding well-separated connected subgraphs; which facilitates the label spreading process. Results on benchmark data sets comparing to state-of-the-art methods show the potential of the proposed algorithm.