Multi-View Treelet Transform
This work addresses a gap in multi-view factorization for hierarchical data, such as in network analysis and neuroscience, though it appears incremental as it extends an existing single-view method.
The authors tackled the problem of capturing hierarchical structure in multi-view data, which existing methods could not handle, by generalizing the Treelet Transform to the Multi-View Treelet Transform (MVTT). They demonstrated its application in denoising empirical networks and computing shared responses in functional brain data.
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.