Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Composite Kernels for Hypertext Categorisation
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient co-regularised least squares regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Linear prediction models with graph regularization for web-page categorization
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast protein classification with multiple networks
Bioinformatics
Proceedings of the 24th international conference on Machine learning
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Clustering with local and global regularization
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Multiple view semi-supervised dimensionality reduction
Pattern Recognition
A general learning framework using local and global regularization
Pattern Recognition
Neurocomputing
Composite hashing with multiple information sources
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Multi-view transfer learning with a large margin approach
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
ShareBoost: boosting for multi-view learning with performance guarantees
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Multi-view learning via probabilistic latent semantic analysis
Information Sciences: an International Journal
LinkFCM: Relation integrated fuzzy c-means
Pattern Recognition
MI2LS: multi-instance learning from multiple informationsources
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Web page and image semi-supervised classification with heterogeneous information fusion
Journal of Information Science
Discriminative feature selection for multi-view cross-domain learning
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The idea of local learning, i.e., classifying a particular example based on its neighbors, has been successfully applied to many semi-supervised and clustering problems recently. However, the local learning methods developed so far are all devised for single-view problems. In fact, in many real-world applications, examples are represented by multiple sets of features. In this paper, we extend the idea of local learning to multi-view problem, design a multi-view local model for each example, and propose a Multi-View Local Learning Regularization (MVLL-Reg) matrix. Both its linear and kernel version are given. Experiments are conducted to demonstrate the superiority of the proposed method over several state-of-the-art ones.