  An underlying assumption in conventional multi-view learning algorithms is
that all views can be simultaneously accessed. However, due to various factors
when collecting and pre-processing data from different views, the streaming
view setting, in which views arrive in a streaming manner, is becoming more
common. By assuming that the subspaces of a multi-view model trained over past
views are stable, here we fine tune their combination weights such that the
well-trained multi-view model is compatible with new views. This largely
overcomes the burden of learning new view functions and updating past view
functions. We theoretically examine convergence issues and the influence of
streaming views in the proposed algorithm. Experimental results on real-world
datasets suggest that studying the streaming views problem in multi-view
learning is significant and that the proposed algorithm can effectively handle
streaming views in different applications.
