Multi-View Clustering Microbiome Data By Joint Symmetric Nonnegative Matrix Factorization With Laplacian Regularization

Many datasets existed in the real world are often comprised of different representations or views which provide complementary information to each other. For example, microbiome datasets can be represented by metabolic paths, taxonomic assignment or gene families. To integrate information from multiple views, data integration approaches such as methods based on nonnegative matrix factorization (NMF) have been developed to combine multi-view information simultaneously to obtain a comprehensive view which reveals the underlying data structure shared by multiple views. In this paper, we proposed a novel variant of symmetric nonnegative matrix factorization (SNMF), called Laplacian regularized joint symmetric nonnegative matrix factorization (LJ-SNMF) for clustering multi-view data. We conduct extensive experiments on several realistic datasets including Human Microbiome Project (HMP) data. The experimental results show that the proposed method outperforms other variants of NMF, which suggests the potential application of LJ-SNMF in clustering multi-view datasets.