A Multi-Modal Fusion Method Based on Higher-Order Orthogonal Iteration Decomposition

Multi-modal fusion can achieve better predictions through the amalgamation of information from different modalities. To improve the performance of accuracy, a method based on Higher-order Orthogonal Iteration Decomposition and Projection (HOIDP) is proposed, in the fusion process, higher-order ortho...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 23; no. 10; p. 1349
Main Authors: Liu, Fen, Chen, Jianfeng, Tan, Weijie, Cai, Chang
Format: Journal Article
Language:English
Published: Basel MDPI AG 15.10.2021
MDPI
Subjects:
Algorithms
Data mining
Datasets
Decomposition
dimensionality reduction
Emotion recognition
Forecasting
iteration decomposition
Iterative methods
multi-modal fusion
Neural networks
Performance enhancement
Sentiment analysis
tensor
ISSN:1099-4300, 1099-4300
Online Access:Get full text
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Summary:Multi-modal fusion can achieve better predictions through the amalgamation of information from different modalities. To improve the performance of accuracy, a method based on Higher-order Orthogonal Iteration Decomposition and Projection (HOIDP) is proposed, in the fusion process, higher-order orthogonal iteration decomposition algorithm and factor matrix projection are used to remove redundant information duplicated inter-modal and produce fewer parameters with minimal information loss. The performance of the proposed method is verified by three different multi-modal datasets. The numerical results validate the accuracy of the performance of the proposed method having 0.4% to 4% improvement in sentiment analysis, 0.3% to 8% improvement in personality trait recognition, and 0.2% to 25% improvement in emotion recognition at three different multi-modal datasets compared with other 5 methods.
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Current address: School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China.
ISSN:1099-4300
1099-4300
DOI:10.3390/e23101349