基于超图正则化的多模态信息融合算法

doi:10.11896/jsjkx.220900144

Abstract

Abstract: The multi-modal data fusion improves the performance of data classification and prediction by learning the correlation information and complementary information between multiple datasets.However,existing data fusion methods are based on feature pattern learning of single dataset and ignore structural information among different heterogeneous datasets.This paper proposes a multi-modal data fusion algorithm based on hypergraph regularization(sHMF),acquiring hyper-order relationship of inter-and cross-modality by combining hypergraph and manifold regularization,i.e.homogeneous and heterogeneous high-order networks.Specifically,it firstly generates a hypergraph similarity matrix to represent the high-order relationships among subjects.In the proposed method,the sparse representation of hypergraph is used to build hypergraph for reducing redundant hype-redges.sHMF is validated on the simulated data and real imaging genetic data of schizophrenia patients.Experiment results show that our algorithm outperforms several widely used methods in the classification accuracy of simulated data and real data,and reveals some biomarkers significantly associated with schizophrenia and the potential links between risk genes,methylation factors and abnormal brain regions.

Key words: Multi-modal data fusion, Manifold regularization, Functional magnetic resonance imaging, Imaging genetics

CLC Number:

TP391

CUI Bingjing, ZHANG Yipu, WANG Biao. Multimodal Data Fusion Algorithm Based on Hypergraph Regularization[J].Computer Science, 2023, 50(6): 167-174.

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Multimodal Data Fusion Algorithm Based on Hypergraph Regularization

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