Elements of information theory
Elements of information theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
Induction of multiple fuzzy decision trees based on rough set technique
Information Sciences: an International Journal
Probabilistic matrix tri-factorization
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
Multi-view regression via canonical correlation analysis
COLT'07 Proceedings of the 20th annual conference on Learning theory
Multi-view clustering of multilingual documents
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A short text modeling method combining semantic and statistical information
Information Sciences: an International Journal
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Ensemble of feature sets and classification algorithms for sentiment classification
Information Sciences: an International Journal
A boosting approach to multiview classification with cooperation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Maximum Ambiguity-Based Sample Selection in Fuzzy Decision Tree Induction
IEEE Transactions on Knowledge and Data Engineering
Learning very fast decision tree from uncertain data streams with positive and unlabeled samples
Information Sciences: an International Journal
Fast dimension reduction for document classification based on imprecise spectrum analysis
Information Sciences: an International Journal
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Multi-view learning arouses vast amount of interest in the past decades with numerous real-world applications in web page analysis, bioinformatics, image processing and so on. Unlike the most previous works following the idea of co-training, in this paper we propose a new generative model for Multi-view Learning via Probabilistic Latent Semantic Analysis, called MVPLSA. In this model, we jointly model the co-occurrences of features and documents from different views. Specifically, in the model there are two latent variables y for the latent topic and z for the document cluster, and three visible variables d for the document, f for the feature, and v for the view label. The conditional probability p(z|d), which is independent of v, is used as the bridge to share knowledge among multiple views. Also, we have p(y|z, v) and p(f|y, v), which are dependent of v, to capture the specifical structures inside each view. Experiments are conducted on four real-world data sets to demonstrate the effectiveness and superiority of our model.