Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
SMEM algorithm for mixture models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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The paper presents a novel multi-view learning framework based on variational inference. We formulate the framework as a graph representation in form of graph factorization: the graph comprises of factor graphs, which are used to describe internal states of views. Each view is modeled with a Gaussian mixture model. The proposed framework has three main advantages 1) less constraint assumed on data, 2) effective utilization of unlabeled data, and 3) automatic data structure inferring: proper data structure can be inferred in only one round. The experiments on image segmentation demonstrate its effectiveness.