# Deep Variational Canonical Correlation Analysis

arXiv: https://arxiv.org/abs/1610.03454 · HTML: https://mlsanity.com/paper/1610.03454/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2016.

## Built on by (most cited first, 30 of 44)

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- [Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data](https://mlsanity.com/paper/2302.06232/) (2023)
- [Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective](https://mlsanity.com/paper/2106.07115/) (2021)
- [Deep Co-Attention Network for Multi-View Subspace Learning](https://mlsanity.com/paper/2102.07751/) (2021)
- [Gacs-Korner Common Information Variational Autoencoder](https://mlsanity.com/paper/2205.12239/) (2022)
- [Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts](https://mlsanity.com/paper/2101.07240/) (2021)
- [A Survey on Deep Clustering: From the Prior Perspective](https://mlsanity.com/paper/2406.19602/) (2024)
- [Disentangled Variational Information Bottleneck for Multiview Representation Learning](https://mlsanity.com/paper/2105.07599/) (2021)
- [Multi-View Independent Component Analysis with Shared and Individual Sources](https://mlsanity.com/paper/2210.02083/) (2022)
- [Interpretability with full complexity by constraining feature information](https://mlsanity.com/paper/2211.17264/) (2022)
- [Towards Robust Unsupervised Disentanglement of Sequential Data -- A Case Study Using Music Audio](https://mlsanity.com/paper/2205.05871/) (2022)
- [Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses](https://mlsanity.com/paper/2310.03311/) (2023)
- [Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learning](https://mlsanity.com/paper/2310.04458/) (2023)
- [Revisiting Deep Generalized Canonical Correlation Analysis](https://mlsanity.com/paper/2312.13455/) (2023)
- [Discriminative Multimodal Learning via Conditional Priors in Generative Models](https://mlsanity.com/paper/2110.04616/) (2021)
- [Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectives](https://mlsanity.com/paper/2309.00380/) (2023)
- [Disentanglement of Variations with Multimodal Generative Modeling](https://mlsanity.com/paper/2509.23548/) (2025)
- [Disentangling shared and private latent factors in multimodal Variational Autoencoders](https://mlsanity.com/paper/2403.06338/) (2024)
- [Identifiable Shared Component Analysis of Unpaired Multimodal Mixtures](https://mlsanity.com/paper/2409.19422/) (2024)
- [Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders](https://mlsanity.com/paper/2505.01134/) (2025)
- [Content-Style Learning from Unaligned Domains: Identifiability under Unknown Latent Dimensions](https://mlsanity.com/paper/2411.03755/) (2024)
- [Accurate Estimation of Mutual Information in High Dimensional Data](https://mlsanity.com/paper/2506.00330/) (2025)
- [Content-Style Identification via Differential Independence](https://mlsanity.com/paper/2605.17827/) (2026)
- [Multimodal Generative Models for Bankruptcy Prediction Using Textual Data](https://mlsanity.com/paper/2211.08405/) (2022)
- [Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis](https://mlsanity.com/paper/2305.11832/) (2023)

## Abstract (arXiv)

We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear observation models parameterized by deep neural networks. We derive variational lower bounds of the data likelihood by parameterizing the posterior probability of the latent variables from the view that is available at test time. We also propose a variant of VCCA called VCCA-private that can, in addition to the "common variables" underlying both views, extract the "private variables" within each view, and disentangles the shared and private information for multi-view data without hard supervision. Experimental results on real-world datasets show that our methods are competitive across domains.