Weighted Multi-View Data Clustering via Joint Non-Negative Matrix Factorization

Khan, Ghufran Ahmad; Hu, Jie; Li, Tianrui; Diallo, Bassoma; Huang, Qianqian

doi:10.1109/iske47853.2019.9170204

Cited by 5 publications

(1 citation statement)

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“…With the advancement of data size, the information assembled from various views generally contain redundant data and high dimensional property that can increment the computational cost. Several matrix factorizations techniques have gain broad consideration, for example principal component analysis (PCA) [11], Singular Value Decomposition (SVD) [12], non-negative matrix factorization (NMF) [13] etc. Among them, NMF draws much attention of the researchers in multi-view cluster analysis [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-View Clustering Based on Multiple Manifold Regularized Non-Negative Sparse Matrix Factorization

Khan

et al. 2022

IEEE Access

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Clustering of multi-view data has got broad consideration of the researchers. Multi-view data is composed through different domain which shows the consistent and complementary behavior. The existing studies did not draw attention of over-fitting and sparsity among the diverse view, which is the considerable issue for getting the unique consensus knowledge from these complementary data. Herein article, a multi-view clustering approach is recommended to provide the consensus solution from the multiview data. To accomplish this task, we exploit non-negative matrix factorized method to generate a cost function. Further, manifold learning model is used to build the graph through the nearest neighbor strategy, which is effective to save the geometrical design for data and feature matrix. Furthermore, the over-fitting problem, sparsity is handled through adaption of frobenious norm, and L 1 -norm on basis and coefficient matrices. The whole formulation is done through the mathematical function, which is optimized through the iterative updating strategy to get the optimal solution. The computational experiment is carried on the available datasets to exhibits that the proposed strategy beats the current methodologies in terms of clustering execution.INDEX TERMS Non-negative matrix factorization, multi-view data, manifold structure, nearest neighbor.

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