Multiview Subspace Learning
In multiview settings, observations from different views are assumed to share the same subspace. The abundance of views can be utilized to better explore the subspace. In this chapter, we consider two different kinds of multiview subspace learning problems. The first one contains the general unsupervised multiview subspace learning problems. We focus on canonical correlation analysis as well as some of its extensions. The second one contains the supervised multiview subspace learning problems, i.e., there exists available label information. In this case, representations more suitable for the on-hand task can be obtained by utilizing the label information. We also briefly introduce some other methods at the end of this chapter.
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