Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs

We consider a problem of grouping multiple graphs into several clusters using singular value thesholding and non-negative factorization. We derive a model selection information criterion to estimate the number of clusters. We demonstrate our approach using "Swimmer data set" as well as simulated data set, and compare its performance with two standard clustering algorithms.Comment: This paper has been withdrawn by the author due to a newer version with overlapping content

Multiple Kernel Driven Clustering With Locally Consistent and Selfish Graph in Industrial IoT

[EN] In the cognitive computing of intelligent industrial Internet of Things, clustering is a fundamental machine learning problem to exploit the latent data relationships. To overcome the challenge of kernel choice for nonlinear clustering tasks, multiple kernel clustering (MKC) has attracted intensive attention. However, existing graph-based MKC methods mainly aim to learn a consensus kernel as well as an affinity graph from multiple candidate kernels, which cannot fully exploit the latent graph information. In this article, we propose a novel pure graph-based MKC method. Specifically, a new graph model is proposed to preserve the local manifold structure of the data in kernel space so as to learn multiple candidate graphs. Afterward, the latent consistency and selfishness of these candidate graphs are fully considered. Furthermore, a graph connectivity constraint is introduced to avoid requiring any postprocessing clustering step. Comprehensive experimental results demonstrate the superiority of our method.This work was supported in part by Sichuan Science and Technology Program under Grant 2020ZDZX0014 and Grant 2019ZDZX0119 and in part by the Key Lab of Film and TV Media Technology of Zhejiang Province under Grant 2020E10015.Ren, Z.; Mukherjee, M.; Lloret, J.; Venu, P. (2021). Multiple Kernel Driven Clustering With Locally Consistent and Selfish Graph in Industrial IoT. IEEE Transactions on Industrial Informatics. 17(4):2956-2963. https://doi.org/10.1109/TII.2020.3010357S2956296317

Self-weighted Multiview Clustering with Multiple Graphs

<p> 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 Laplacian rank constrained graph, which can be approximately as the centroid of the built graph for each view with different confidences. We start our work with a natural thought that the weights can be learned by introducing a hyperparameter. By analyzing the weakness of this way, we further propose a new multiview clustering method which is totally selfweighted. More importantly, once the target graph is obtained in our models, we can directly assign the cluster label to each data point and do not need any postprocessing such as K-means in standard spectral clustering. Evaluations on two synthetic datasets indicate the effectiveness of our methods. Compared with several representative graphbased multiview clustering approaches on four realworld datasets, the proposed methods achieve the better performances and our new clustering method is more practical to use.</p