期刊
FRONTIERS IN MOLECULAR BIOSCIENCES
卷 8, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fmolb.2021.643014
关键词
matrix factorization; Laplacian regularization; Vicus graph; microbiome; local structure
资金
- Key Research Projects of Henan Higher Education Institutions [20B520002]
- Key Technology R&D Program of Henan Province [202102310561]
In this paper, a new algorithm LVSNMF is introduced which simultaneously utilizes Laplacian and Vicus matrices to capture both global and local structure patterns. Experimental results demonstrate its superior performance in biological data analysis.
A network is an efficient tool to organize complicated data. The Laplacian graph has attracted more and more attention for its good properties and has been applied to many tasks including clustering, feature selection, and so on. Recently, studies have indicated that though the Laplacian graph can capture the global information of data, it lacks the power to capture fine-grained structure inherent in network. In contrast, a Vicus matrix can make full use of local topological information from the data. Given this consideration, in this paper we simultaneously introduce Laplacian and Vicus graphs into a symmetric non-negative matrix factorization framework (LVSNMF) to seek and exploit the global and local structure patterns that inherent in the original data. Extensive experiments are conducted on three real datasets (cancer, cell populations, and microbiome data). The experimental results show the proposed LVSNMF algorithm significantly outperforms other competing algorithms, suggesting its potential in biological data analysis.
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