Cross-Dimension and Cross-Modality Relational Tensor Representation Learning for Multimodal Health Data Modeling

Key points

• Multimodal health data integrate information from heterogeneous clinical sources, creating substantial opportunities for data-driven decision support.1
• RTR introduces learnable relational bridge matrices that maintain relational information while preserving the structural identities of tensor dimensions and modalities.1
• Experiments on simulated data and two multimodal neurophysiological datasets, covering emotion recognition and brain activity classification, demonstrate that RTR consistently outperforms representative tensor learning, multimodal fusion, and Transformer-based baselines.1
• These results highlight the value of explicitly modeling cross-dimensional and cross-modality relationships for learning informative representations from heterogeneous multimodal tensor data.1

Sources (1)

• [1]Cross-Dimension and Cross-Modality Relational Tensor Representation Learning for Multimodal Health Data Modeling
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 03:31 AM

  This paper proposes Relational Tensor Representation (RTR), a unified framework that jointly learns dimension-specific tensor representations and cross-dimensional and cross-modality relationships.

  Multimodal health data integrate information from heterogeneous clinical sources, creating substantial opportunities for data-driven decision support.

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