Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised learning by mixed label propagation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
KNN-kernel density-based clustering for high-dimensional multivariate data
Computational Statistics & Data Analysis
Using weighted nearest neighbor to benefit from unlabeled data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
On the utility of partially labeled data for classification of microarray data
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Unlabeling data can improve classification accuracy
Pattern Recognition Letters
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In the k -nearest neighbor (KNN) classifier, nearest neighbors involve only labeled data. That makes it inappropriate for the data set that includes very few labeled data. In this paper, we aim to solve the classification problem by applying transduction to the KNN algorithm. We consider two groups of nearest neighbors for each data point -- one from labeled data, and the other from unlabeled data. A kernel function is used to assign weights to neighbors. We derive the recurrence relation of neighboring data points, and then present two solutions to the classification problem. One solution is to solve it by matrix computation for small or medium-size data sets. The other is an iterative algorithm for large data sets, and in the iterative process an energy function is minimized. Experiments show that our solutions achieve high performance and our iterative algorithm converges quickly.