Multi-View Treelet Transform
Machine Learning
2016-06-20 v2 Computer Vision and Pattern Recognition
Social and Information Networks
Neurons and Cognition
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.
Keywords
Cite
@article{arxiv.1606.00800,
title = {Multi-View Treelet Transform},
author = {Brian A. Mitchell and Linda R. Petzold},
journal= {arXiv preprint arXiv:1606.00800},
year = {2016}
}