  Current multi-view factorization methods make assumptions that are not
acceptable for many kinds of data, and in particular, for graphical data with
hierarchical structure. At the same time, current hierarchical methods work
only in the single-view setting. We generalize the Treelet Transform to the
Multi-View Treelet Transform (MVTT) to allow for the capture of hierarchical
structure when multiple views are available. Further, we show how this
generalization is consistent with the existing theory and how it might be used
in denoising empirical networks and in computing the shared response of
functional brain data.
