Biomedical Data Analysis Based on Multi-view Intact Space Learning with Geodesic Similarity Preserving

Diverse, high-dimensional modalities collected in large cohorts present new opportunities for the formulation and testing of integrative scientific hypotheses. Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces. SiMLR contributes an objective function for identifying joint signal, regularization based on sparse matrices representing prior within-modality relationships and an implementation that permits application to joint reduction of large data matrices. We demonstrate that SiMLR outperforms closely related methods on supervised learning problems in simulation data, a multi-omics cancer survival prediction dataset and multiple modality neuroimaging datasets. Taken together, this collection of results shows that SiMLR may be applied to joint signal estimation from disparate modalities and may yield practically useful results in a variety of application domains.

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Driving Stress Estimation in Physiological Signals Based on Hierarchical Clustering and Multi-View Intact Space Learning

Runqiang

Chen

2022

IEEE Trans. Intell. Transport. Syst.

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Biomedical Data Analysis Based on Multi-view Intact Space Learning with Geodesic Similarity Preserving

Cited by 3 publications

References 37 publications

Multi-modal bioelectrical signal fusion analysis based on different acquisition devices and scene settings: Overview, challenges, and novel orientation

Multi-modal bioelectrical signal fusion analysis based on different acquisition devices and scene settings: Overview, challenges, and novel orientation

Similarity-driven multi-view embeddings from high-dimensional biomedical data

Driving Stress Estimation in Physiological Signals Based on Hierarchical Clustering and Multi-View Intact Space Learning

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