Multimodal Fusion Networks Based on Sparse Selfencoders

Multimodal data fusion faces significant challenges due to heterogeneous representations, imbalanced information distribution, and ambiguous inter-modal relationships. Realworld multimodal data often exhibit incompleteness and uncertain associations, where textual data manifest as sparse discrete vectors while visual data appear as dense continuous features. Effective fusion frameworks must accommodate missing modalities and enable robust similarity computation. To address these challenges, we propose a sparse autoencoder-based fusion architecture that integrates text and visual modalities. Unlike conventional autoencoders for feature learning, our approach employs sparsity constraints to handle high-dimensional data and modality imbalance. The core contribution is an efficient fusion framework that learns shared representations through joint reconstruction and classification objectives. Experimental validation on fake news detection tasks demonstrates superior performance over state-of-the-art baselines.