# Discriminative Multiple Canonical Correlation Analysis for Information Fusion

arXiv: https://arxiv.org/abs/2103.00361 · HTML: https://mlsanity.com/paper/2103.00361/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 54 references that are in the map.
- First posted on arXiv 28 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [A Closed Form Solution to Multi-View Low-Rank Regression](https://mlsanity.com/paper/1610.04668/) (2016)
- [Discriminative extended canonical correlation analysis for pattern set matching](https://mlsanity.com/paper/1306.2100/) (2013)

## Built on by (most cited first, 6 of 6)

- [Multi-modal Fusion Technology based on Vehicle Information: A Survey](https://mlsanity.com/paper/2211.06080/) (2022)
- [Joint Self-Supervised and Supervised Contrastive Learning for Multimodal MRI Data: Towards Predicting Abnormal Neurodevelopment](https://mlsanity.com/paper/2312.15064/) (2023)
- [A Discriminative Vectorial Framework for Multi-modal Feature Representation](https://mlsanity.com/paper/2103.05597/) (2021)
- [ODMTCNet: An Interpretable Multi-view Deep Neural Network Architecture for Image Feature Representation](https://mlsanity.com/paper/2110.14830/) (2021)
- [Cooperative Sentiment Agents for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2404.12642/) (2024)
- [Towards Achieving Perfect Multimodal Alignment](https://mlsanity.com/paper/2503.15352/) (2025)

## Abstract (arXiv)

In this paper, we propose the Discriminative Multiple Canonical Correlation Analysis (DMCCA) for multimodal information analysis and fusion. DMCCA is capable of extracting more discriminative characteristics from multimodal information representations. Specifically, it finds the projected directions which simultaneously maximize the within-class correlation and minimize the between-class correlation, leading to better utilization of the multimodal information. In the process, we analytically demonstrate that the optimally projected dimension by DMCCA can be quite accurately predicted, leading to both superior performance and substantial reduction in computational cost. We further verify that Canonical Correlation Analysis (CCA), Multiple Canonical Correlation Analysis (MCCA) and Discriminative Canonical Correlation Analysis (DCCA) are special cases of DMCCA, thus establishing a unified framework for Canonical Correlation Analysis. We implement a prototype of DMCCA to demonstrate its performance in handwritten digit recognition and human emotion recognition. Extensive experiments show that DMCCA outperforms the traditional methods of serial fusion, CCA, MCCA and DCCA.