Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Semi-Supervised Learning
| Hi-index | 0.00 |
This paper proposes a graph based Semi-Supervised Learning (SSL) approach by constructing a graph using a metric learning technique. It is important for SSL with a graph to calculate a good distance metric, which is crucial for many high-dimensional data sets, such as image classification. In this paper, we construct the similarity affinity matrix (graph) with the metric optimized by using Adaptive Metric Learning (AML) which performs clustering and distance metric learning simultaneously. Experimental results on real-world datasets show that the proposed algorithm is significantly better than graph based SSL algorithms in terms of classification accuracy, and AML gives a good distance metric to calculate the similarity of the graph. In eight benchmark datasets, 1 to 11 percent is attributed to the improvement of classification accuracy of state of the art graph based approaches.