# Higher Order Correlation Analysis for Multi-View Learning

arXiv: https://arxiv.org/abs/2201.11949 · HTML: https://mlsanity.com/paper/2201.11949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 45 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction](https://mlsanity.com/paper/1502.02330/) (2015)
- [A Tutorial on Canonical Correlation Methods](https://mlsanity.com/paper/1711.02391/) (2017)
- [Semidefinite Relaxations for Best Rank-1 Tensor Approximations](https://mlsanity.com/paper/1308.6562/) (2013)
- [A Complete Semidefinite Algorithm for Detecting Copositive Matrices and Tensors](https://mlsanity.com/paper/1711.03704/) (2017)

## Abstract (arXiv)

Multi-view learning is frequently used in data science. The pairwise correlation maximization is a classical approach for exploring the consensus of multiple views. Since the pairwise correlation is inherent for two views, the extensions to more views can be diversified and the intrinsic interconnections among views are generally lost. To address this issue, we propose to maximize higher order correlations. This can be formulated as a low rank approximation problem with the higher order correlation tensor of multi-view data. We use the generating polynomial method to solve the low rank approximation problem. Numerical results on real multi-view data demonstrate that this method consistently outperforms prior existing methods.