Abstract
In most real applications, data are represented by a single view, which is difficult to apply in multiview learning. In this Chapter, we introduce six view construction methods to generate new views from the originate view. All of them will meet the assumptions of multiview models. Ideally, the hypotheses from two views ought to agree on the same example corresponding to the view consensus principle. Views are thought to be conditionally independent with each other. The simplest method is to partition feature set into disjoint subsets, each of which represents one view. The second method is to purify the high-dimension data to small sets of features as new views. By contrast, one can also generate a new view by adding noise into the originate data. The next three methods are based on neural networks. Reversed sequence can be regarded as another view in sequential models. New views can also be constructed using different modules such as kernel functions, neural networks, filters and other structures which can extract specific features from the original data. Finally, we introduce how to generate a new view conditioned on auxiliary information by conditional generative models.
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Sun, S., Mao, L., Dong, Z., Wu, L. (2019). View Construction. In: Multiview Machine Learning. Springer, Singapore. https://doi.org/10.1007/978-981-13-3029-2_9
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DOI: https://doi.org/10.1007/978-981-13-3029-2_9
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