In multiview learning, it is essential to assign a reasonable weight to each view according to the view importance. Thus, for multiview clustering task, a wise and elegant method should achieve clustering multiview data while learning the view weights. In this paper, we propose to explore a ...
Multiple kernel clustering with local kernel reconstruction and global heat diffusion 2024, Information Fusion Citation Excerpt : The MKC-NCKL approaches are characterized by the extraction of multiple graphs from candidate kernels and their integration into a consensus graph, eliminating the need for int...
Hou C, Nie F, Tao H, Yi D (2017) Multi-view unsupervised feature selection with adaptive similarity and view weight. IEEE Trans Knowl Data Eng 29(9):1998–2011 Article Google Scholar Nie F, Li J, Li X, et al. (2017) Self-weighted multiview clustering with multiple graphs. In: IJ...
IJCAI17: Self-Weighted Multiview Clustering with Multiple Graphs"Papercode TKDE2018: One-step multi-view spectral clusteringPapercode TKDE19: GMC: Graph-based Multi-view ClusteringPapercode ICDM2019: Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multi-view ClusteringPapercode ...
These papers primarily consider unilayer binary undirected graphs which limits their application beyond this framework. As noted by Kivelä et al. [36], “most real and engineered systems include multiple subsystems and layers of connectivity, and developing a deep understanding of multilayer systems...
Graphs Each graph consists of a set of nodes connected to each other with a set of edges. In single-cell RNA sequencing, nodes are cells, and edges are determined according to cell–cell pairwise distances. Heuristic optimization A method for solving a problem that is designed to sacrifice ...
The chromosomes were either distributed throughout the intact nucleus in multiple clusters before NEBD (43% of oocytes) (Fig. 1a and Supplementary Video 1), or associated with the nucleolus and hence partially clustered before NEBD (57% of oocytes) (Extended Data Fig. 1a and Supplementary ...
a C-score value can be calculated for each preprocessing method based on the two graphs, which is able to discriminate the best preprocessing method effectively and stably for the SC3 clustering algorithm. When tested on eight frequently used single-cell gene expression data sets, SC3-e performed...
As a result, the proposed greedy algorithm enables each user to find the biclique with the largest number of edges. CR users then exchange their biclique graphs and compare them in terms of channel size and cluster size. The CR users in proximity will converge to the largest biclique ...
and the Email network are represented asundirected graphsand Twitter and Google+ as directed graphs. For directed graphs, the number of components have two values, the first one being weakly connected and the second one strongly connected. Also, the power-law exponent, average local clustering coe...