Multi-View Treelet Transform

TL;DR

The paper introduces the Multi-View Treelet Transform (MVTT), a novel method for capturing hierarchical structures across multiple data views, applicable to graph data and brain imaging, extending existing single-view hierarchical techniques.

Contribution

It generalizes the Treelet Transform to multiple views, enabling hierarchical analysis in multi-view data, with theoretical consistency and practical applications in network denoising and brain response analysis.

Findings

Effective hierarchical structure capture in multi-view data.

Application to denoising empirical networks.

Use in computing shared brain responses.

Abstract

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.