IEEE/ACM Trans Comput Biol Bioinform. 2020 May-Jun;17(3):788-795. doi: 10.1109/TCBB.2017.2756628. Epub 2017 Sep 26.
Many datasets that exists in the real world are often comprised of different representations or views which provide complementary information to each other. To integrate information from multiple views, data integration approaches such as nonnegative matrix factorization (NMF) have been developed to combine multiple heterogeneous data simultaneously to obtain a comprehensive representation. In this paper, we proposed a novel variant of symmetric nonnegative matrix factorization (SNMF), called Laplacian regularization based joint symmetric nonnegative matrix factorization (LJ-SNMF) for clustering multi-view data. We conduct extensive experiments on several realistic datasets including Human Microbiome Project 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. Additionally, we also demonstrate the capability of LJ-SNMF in community finding.
许多存在于现实世界中的数据集通常由不同的表示或视图组成，这些视图彼此提供互补信息。为了整合来自多个视图的信息，已经开发了数据集成方法，如非负矩阵分解（NMF），以同时组合多个异构数据，从而获得全面的表示。在本文中，我们提出了一种新的对称非负矩阵分解（SNMF）变体，称为基于拉普拉斯正则化的联合对称非负矩阵分解（LJ-SNMF），用于聚类多视图数据。我们在包括人类微生物组计划数据在内的几个真实数据集上进行了广泛的实验。实验结果表明，所提出的方法优于 NMF 的其他变体，这表明 LJ-SNMF 在聚类多视图数据集方面具有潜在的应用。此外，我们还展示了 LJ-SNMF 在社区发现方面的能力。
