ArticleFrontiers in neuroscience2021
Co-sparse Non-negative Matrix Factorization.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- A novel prognostic biomarker combiningOncology letters · 2026Article
- Integration of latent factor analysis into multivariable Mendelian randomization.European journal of epidemiology · 2026Article
- Multi-omics and experimental validation unveil BDNF as a diagnostic biomarker and therapeutic target in endoplasmic reticulum stress-driven lung adenocarcinoma: Therapeutic potential of Esketamine.Oncology letters · 2025Article
- Mechanisms of synergistic regulation of the tumor microenvironment by fibroblasts and keratinocytes in esophageal squamous cell carcinoma.Discover oncology · 2025Article
- Development and validation of disulfidptosis-related genes signature for patients with glioma.Discover oncology · 2024Article
- Necroptosis-related KLRB1 was a potent tumor suppressor and immunotherapy determinant in breast cancer.Heliyon · 2024Article
- Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.Briefings in bioinformatics · 2022Review
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4 authors.
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Abstract
Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible interpretability with non-negativity property. Nowadays, especially in the neuroimaging field, it is common to have at least thousands of voxels while the sample size is only hundreds. The non-negative matrix factorization encounters both computational and theoretical challenge with such high-dimensional data, i.e., there is no guarantee for a sparse and part-based representation of data. To this end, we introduce a co-sparse non-negative matrix factorization method to high-dimensional data by simultaneously imposing sparsity in both two decomposed matrices. Instead of adding some sparsity induced penalty such as
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