We present a new multi-view clustering algorithm, called dual-graph regularized concept factorization for multi-view clustering (MVDGCF).
Matrix factorization is an important technology that obtains the latent representation of data by mining the potential structure of data.
In this paper, we propose a novel algorithm, called dual-graph regularized concept factorization for clustering. (GCF), which simultaneously considers the ...
Oct 22, 2024 · In this paper, we propose a novel algorithm, called dual-graph regularized concept factorization for clustering (GCF), which simultaneously ...
Jun 22, 2024 · Dual-graph regularization is incorporated to form the geometrical framework of the feature and data manifolds, and representation matrix is ...
Oct 22, 2024 · In this approach, each cluster is a linear combination of data points, and each data point is a linear combination of cluster centers. Therefore ...
Feb 22, 2023 · In this paper, we present a robust dual-graph regularized deep matrix factorization (RDDMF) approach for multi-view clustering. Specifically, it ...
Feb 22, 2023 · In this paper, we present a robust dual-graph regularized deep matrix factorization (RDDMF) approach for multi-view clustering.
In this paper, we propose a semisupervised NMF called dual-graph-regularization-constrained nonnegative matrix factorization with label discrimination (DCNMFLD) ...
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Jul 16, 2024 · The proposed approach applies multiple graph regularization constraints to extract manifold information from different views. Kernel technology ...