Hierarchical Multi-view Fisher Discriminant Analysis
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Sparse Semi-supervised Learning Using Conjugate Functions
The Journal of Machine Learning Research
View construction for multi-view semi-supervised learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Multi-view laplacian support vector machines
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
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For multi-view learning, existing methods usually exploit originally provided features for classifier training, which ignore the latent correlation between different views. In this paper, semantic features integrating information from multiple views are extracted for pattern representation. Canonical correlation analysis is used to learn the representation of semantic spaces where semantic features are projections of original features on the basis vectors of the spaces. We investigate the feasibility of semantic features on two learning paradigms: semi-supervised learning and active learning. Experiments on text classification with two state-of-the-art multi-view learning algorithms co-training and co-testing indicate that this use of semantic features can lead to a significant improvement of performance.