Abstract
Multiview supervised learning algorithm can exploit the multiview nature of the data by the consensus of the views, that is, to seek predictors from different views that agree on the same example. In this chapter, we introduce three categories of multiview supervised learning methods. The first one contains the multiview large margin-based classifiers, which regularize the classifiers from different views with their agreements on classification margins to enforce view consensus. The second one contains multiple kernel learning, where the feature mappings underlying multiple kernels will map the views to new feature spaces where the classifiers are more likely to agree on the views. The third one contains some Gaussian process related models, in which case the predict functions themselves are taken as random variables. We also briefly introduce some other methods at the end of this chapter.
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Sun, S., Mao, L., Dong, Z., Wu, L. (2019). Multiview Supervised Learning. In: Multiview Machine Learning. Springer, Singapore. https://doi.org/10.1007/978-981-13-3029-2_4
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DOI: https://doi.org/10.1007/978-981-13-3029-2_4
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