Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Composite Kernels for Hypertext Categorisation
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Solving Sparse, Symmetric, Diagonally-Dominant Linear Systems in Time 0(m1.31)
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd 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
A reference collection for web spam
ACM SIGIR Forum
Transductive rademacher complexity and its applications
COLT'07 Proceedings of the 20th annual conference on Learning theory
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Learning multiple graphs for document recommendations
Proceedings of the 17th international conference on World Wide Web
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
Identifying biologically relevant genes via multiple heterogeneous data sources
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Annotating photo collections by label propagation according to multiple similarity cues
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Multiview clustering: a late fusion approach using latent models
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Multiple view semi-supervised dimensionality reduction
Pattern Recognition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Convex Mixture Models for Multi-view Clustering
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Fighting webspam: detecting spam on the graph via content and link features
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Build Chinese emotion lexicons using a graph-based algorithm and multiple resources
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Unified tag analysis with multi-edge graph
Proceedings of the international conference on Multimedia
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiple view clustering using a weighted combination of exemplar-based mixture models
IEEE Transactions on Neural Networks
Multiple hypergraph clustering of web images by mining Word2Image correlations
Journal of Computer Science and Technology
Fusing heterogeneous modalities for video and image re-ranking
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Simultaneous similarity learning and feature-weight learning for document clustering
TextGraphs-6 Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
Multi-view random walk framework for search task discovery from click-through log
Proceedings of the 20th ACM international conference on Information and knowledge management
Multi-modal constraint propagation for heterogeneous image clustering
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Robust Video Content Analysis via Transductive Learning
ACM Transactions on Intelligent Systems and Technology (TIST)
A bootstrapping method for learning from heterogeneous data
FGIT'11 Proceedings of the Third international conference on Future Generation Information Technology
Community detection via heterogeneous interaction analysis
Data Mining and Knowledge Discovery
Multi-view learning via probabilistic latent semantic analysis
Information Sciences: an International Journal
An application of the self-organizing map to multiple view unsupervised learning
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Nonrigid Matching of Undersampled Shapes via Medial Diffusion
Computer Graphics Forum
Modalities consensus for multi-modal constraint propagation
Proceedings of the 20th ACM international conference on Multimedia
Improving document clustering using automated machine translation
Proceedings of the 21st ACM international conference on Information and knowledge management
CoNet: feature generation for multi-view semi-supervised learning with partially observed views
Proceedings of the 21st ACM international conference on Information and knowledge management
Global Similarity in Social Networks with Typed Edges
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Image classification with manifold learning for out-of-sample data
Signal Processing
Producing a unified graph representation from multiple social network views
Proceedings of the 5th Annual ACM Web Science Conference
Flexible and robust co-regularized multi-domain graph clustering
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-view embedding learning for incompletely labeled data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We consider spectral clustering and transductive inference for data with multiple views. A typical example is the web, which can be described by either the hyperlinks between web pages or the words occurring in web pages. When each view is represented as a graph, one may convexly combine the weight matrices or the discrete Laplacians for each graph, and then proceed with existing clustering or classification techniques. Such a solution might sound natural, but its underlying principle is not clear. Unlike this kind of methodology, we develop multiview spectral clustering via generalizing the normalized cut from a single view to multiple views. We further build multiview transductive inference on the basis of multiview spectral clustering. Our framework leads to a mixture of Markov chains defined on every graph. The experimental evaluation on real-world web classification demonstrates promising results that validate our method.