TY  - EJOU
AU  - Liu, Fen 
AU  - Zhang, Jinghua 
AU  - Tan, Weijie 

TI  - STP-BTDM: Semi-Tensor Product-Based Block Term Decomposition of Multilinear Pooling Method for Multi-Modal Information Fusion in Sentiment Analysis
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 89
IS  - 2
SN  - 1546-2226

AB  - Multi-modal information fusion integrates data from various sensors, distinct sources, or different modalities, such as audio, images, and text, to achieve a more comprehensive and accurate understanding and analysis. This paper proposes a Semi-Tensor Product-based Block Term Decomposition of Multilinear (STP-BTDM) pooling method and applies it to sentiment analysis and emotion recognition. Unlike prior factorized multilinear approaches, STP-BTDM introduces block-term decomposition with a block-diagonal core tensor, yielding a globally sparse yet locally dense structure and enabling modality-specific independent subspace learning. The technique first introduces the Semi-Tensor Product-based Block Term Decomposition (STP-BTD) model to obtain globally sparse and locally dense weight tensors. Subsequently, by combining the multilinear pooling model, the STP-BTDM method is presented. This approach allows each modality to be controlled by only one block of the block-diagonal core, with different blocks being independent during training. The model can represent full multilinear interactions in a computationally efficient manner. Moreover, the introduction of sparsity constraints in the core tensor of STP-BTDM enhances the generalization performance of multilinear pooling. The resulting locally dense yet globally sparse characteristics make the model highly flexible. Finally, our experiments with the STP-BTDM method on the CMU-MOSI dataset for sentiment analysis and the IEMOCAP dataset for emotion recognition demonstrate its superior performance and effectiveness across both tasks.
KW  - Semi-tensor product; block term decomposition; multilinear pooling; multi-modal information fusion

DO  - 10.32604/cmc.2026.086571