Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
The Effect of the Input Density Distribution on Kernel-based Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Improved Nyström low-rank approximation and error analysis
Proceedings of the 25th international conference on Machine learning
Clustered Nyström method for large scale manifold learning and dimension reduction
IEEE Transactions on Neural Networks
Efficient manifold ranking for image retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Speedy local search for semi-supervised regularized least-squares
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Unsupervised face-name association via commute distance
Proceedings of the 20th ACM international conference on Multimedia
Adjacency matrix construction using sparse coding for label propagation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Semi-Supervised learning on a budget: scaling up to large datasets
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Local learning integrating global structure for large scale semi-supervised classification
Computers & Mathematics with Applications
Convex and scalable weakly labeled SVMs
The Journal of Machine Learning Research
Sparse semi-supervised learning on low-rank kernel
Neurocomputing
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Practical data mining rarely falls exactly into the supervised learning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computational intensiveness of graph-based SSL arises largely from the manifold or graph regularization, which in turn lead to large models that are difficult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highly scalable, graph-based algorithm for large-scale SSL. Our key innovation is the use of "prototypes vectors" for efficient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of the kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number of machine learning benchmark data sets.