Scalable Multi-Modal Representation Learning Networks

Information Science Artificial Intelligence Computer Science
multi-modal learning representation learning inductive models sparse regularization hypergraph embedding

Academic Background

In the field of artificial intelligence, Multi-modal Representation Learning (MMRL) is a powerful paradigm aimed at mapping inputs from different modalities into a shared representation space. For example, in social networks, users often share both images and text simultaneously. Through multi-modal representation learning, models can better understand the relationship between certain words or concepts in the text and visual patterns in the images. This paradigm has been widely applied in various domains such as healthcare and emotion recognition, as data often exists in multiple forms, and the fusion of multi-modal information can enhance the overall understanding and decision-making capabilities of systems.

However, existing multi-modal representation learning methods face two main challenges: high-order information preservation and out-of-sample data generalization. Firstly, existing methods primarily consider pairwise standard graph structures, overlooking the potential insights that high-order relationships might provide. Secondly, most existing graph-based multi-modal representation learning frameworks assume that complete multi-modal data has been collected at the inference stage, whereas real-world inference scenarios are dynamic, and existing frameworks often neglect the testing of newly generated multi-modal samples. These issues limit the scalability and efficiency of existing methods in practical applications.

To address these problems, a research team from Fuzhou University proposed a Scalable Multi-modal Representation Learning Networks (SMMRL) framework. This framework aims to learn optimal modality-specific projection matrices to project multi-modal features into a shared representation space, thereby achieving high-order information preservation and out-of-sample data generalization.

Source of the Paper

This paper was co-authored by Zihan Fang, Ying Zou, Shiyang Lan, Shide Du, Yanchao Tan, and Shiping Wang, all from the College of Computer and Data Science at Fuzhou University. The paper was accepted on April 4, 2025, and published in the journal Artificial Intelligence Review under the title Scalable Multi-modal Representation Learning Networks. The code for the paper has been made publicly available on GitHub for researchers and developers to use.

Research Process

1. Problem Definition and Objectives

The research team first defined the two main challenges in multi-modal representation learning: high-order information preservation and out-of-sample data generalization. To address these issues, they proposed the SMMRL framework, which achieves its objectives through the following three main contributions: 1. A high-order correlation-preserving feature selection model was proposed, mapping multi-modal data into a consensus representation space through row-sparsity constrained projection. 2. A proximal operator-inspired network architecture was designed, encoding sparsity and hypergraph embedding as prior knowledge into the network structure. 3. Extensive evaluation was conducted on multi-modal tasks, including out-of-sample data extension, demonstrating the effectiveness and superiority of the learned modality-consensus representation.

2. Methodology

2.1 Mathematical Formulation

The research team first defined the mathematical representation of multi-modal data. Assuming multi-modal data from M modalities, each with a feature dimension of dm and n samples, the team constructed an optimization model by defining modality-specific projection matrices and modality-consensus representation matrices. The objective was to minimize the projection error and regularization terms, which included row-sparsity constraints and hypergraph Laplacian regularization to ensure that similar data points have similar coefficients in the representation space.

2.2 Optimization Solution

To solve the optimization problem, the research team employed the Proximal Operator method. The proximal operator is used to impose sparsity constraints on variables during the optimization process and iteratively update the projection and representation matrices. Specifically, the team transformed the optimization objectives into trainable neural network modules through a proximal operator-inspired network architecture, enabling joint training of feature auto-weighted selection and representation learning.

2.3 Learnable Network Architecture

The research team treated the iterative optimization algorithm as a recurrent neural network, where the k-th iteration corresponds to the k-th layer in a feedforward network. By introducing learnable weights and activation functions, the team designed a deep neural network architecture that automatically updates modality-specific projection matrices and representation matrices. Finally, the team updated the network parameters using a cross-entropy loss function, gradually optimizing the model’s performance during training.

3. Experiments and Evaluation

The research team conducted extensive experiments on six real-world multi-modal datasets to evaluate the effectiveness and superiority of the SMMRL framework. The experimental design aimed to answer four key research questions: 1. Experimental Results and Analysis: How does SMMRL perform in terms of quantitative metrics compared to state-of-the-art methods? 2. Scalability Validation: Does SMMRL achieve high-order relationship preservation and out-of-sample data generalization? 3. Model Analysis: What is the impact of hyperparameters and different fusion strategies on performance, and how can optimal parameter values be selected? 4. Convergence Behavior and Training Efficiency: What is the practicality and effectiveness of SMMRL?

3.1 Experimental Setup

The research team adopted two different learning paradigms: transductive learning and inductive learning. In transductive learning, the model constructs a hypergraph structure using all available data but computes the loss function only for the labeled portion. In inductive learning, the model is trained using a limited set of labeled examples and, after training, directly maps unseen data into the representation space for classification using the learned projection matrix.

3.2 Datasets

The research team conducted experiments on six real-world multi-modal datasets, including BDGP, Flickr, ESP-Game, HW, NUS-WIDE, and Reuters. These datasets cover various types of data, such as vision-language data, digit images, and document collections.

3.3 Compared Methods

To evaluate the effectiveness of SMMRL, the research team compared it with seven state-of-the-art multi-modal representation learning methods, including DHGNN, HGNN, HLR-M2VS, IMVGCN, and ORLNet. The experimental results showed that SMMRL outperformed most methods on the majority of datasets, particularly in high-order information preservation and out-of-sample data generalization.

4. Results and Discussion

4.1 Experimental Results and Analysis

The experimental results showed that SMMRL achieved the best or second-best performance on most datasets. Particularly on the HW and NUS-WIDE datasets, SMMRL demonstrated outstanding performance, significantly outperforming other comparison methods. By visualizing the learned modality-consensus representations, the research team found that SMMRL could better separate samples of different classes and maintain clear clustering structures in the representation space.

4.2 Scalability Validation

To validate the scalability of SMMRL, the research team conducted variant analysis and out-of-sample data testing. The experimental results showed that SMMRL excelled in both high-order information preservation and out-of-sample data generalization. Particularly in out-of-sample data testing, SMMRL maintained stable performance across different training ratios, demonstrating its strong generalization capability.

4.3 Model Analysis

The research team further explored the impact of the number of network layers and regularization parameters on the performance of SMMRL. The experimental results showed that classification accuracy initially improved with an increasing number of layers but stabilized after reaching a certain depth. Additionally, SMMRL was relatively insensitive to the value of the regularization parameter λ, indicating its robustness in handling high-dimensional data.

4.4 Fusion Strategy

The research team also investigated the impact of different fusion strategies on the performance of SMMRL. The experimental results showed that the weighted fusion strategy performed best on most datasets, particularly in handling high-dimensional data, where weighted fusion effectively integrated multi-modal information, enhancing the overall performance of the model.

5. Conclusion

Unlike traditional multi-modal representation learning methods, SMMRL effectively addresses the issues of high-order information preservation and out-of-sample data generalization by introducing hypergraph embedding and a proximal operator-inspired network architecture. The research team conducted extensive experiments on multiple real-world datasets, and the results demonstrated that SMMRL performs exceptionally well in handling multi-modal data, particularly in high-order information preservation and out-of-sample data generalization. This research provides new ideas and methods for the field of multi-modal representation learning, with significant scientific value and application prospects.

Research Highlights

  1. High-order Information Preservation: By introducing hypergraph embedding, SMMRL effectively captures high-order correlations among multi-modal samples, thereby enhancing the quality of representation learning.
  2. Out-of-sample Data Generalization: SMMRL, through the design of feature auto-weighted selection and modality-specific projection matrices, effectively transfers knowledge from known data to out-of-sample data, demonstrating strong generalization capabilities.
  3. Scalability: SMMRL performs exceptionally well in handling large-scale multi-modal datasets, particularly in high-dimensional data and high-order relationship modeling, with high computational efficiency and scalability.

Research Value

The SMMRL framework provides new solutions for the field of multi-modal representation learning, particularly in high-order information preservation and out-of-sample data generalization. This research is not only theoretically innovative but also demonstrates broad application prospects in practical scenarios, especially in social networks, healthcare, and emotion recognition. By making the code and datasets publicly available, the research team has provided valuable resources and references for subsequent studies.