# Variational Interpretable Learning from Multi-view Data

arXiv: https://arxiv.org/abs/2202.13503 · HTML: https://mlsanity.com/paper/2202.13503/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [On Deep Multi-View Representation Learning: Objectives and Optimization](https://mlsanity.com/paper/1602.01024/) (2016)
- [Deep Variational Canonical Correlation Analysis](https://mlsanity.com/paper/1610.03454/) (2016)
- [Randomized Nonlinear Component Analysis](https://mlsanity.com/paper/1402.0119/) (2014)
- [Group Factor Analysis](https://mlsanity.com/paper/1411.5799/) (2014)
- [Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis](https://mlsanity.com/paper/1708.04673/) (2017)
- [Variational Inference for Deep Probabilistic Canonical Correlation Analysis](https://mlsanity.com/paper/2003.04292/) (2020)

## Abstract (arXiv)

The main idea of canonical correlation analysis (CCA) is to map different views onto a common latent space with maximum correlation. We propose a deep interpretable variational canonical correlation analysis (DICCA) for multi-view learning. The developed model extends the existing latent variable model for linear CCA to nonlinear models through the use of deep generative networks. DICCA is designed to disentangle both the shared and view-specific variations for multi-view data. To further make the model more interpretable, we place a sparsity-inducing prior on the latent weight with a structured variational autoencoder that is comprised of view-specific generators. Empirical results on real-world datasets show that our methods are competitive across domains.