Sep 1, 2024 · In order to obtain better clustering performance based on NMF, manifold and orthogonal constraint, a new type of model named Orthogonal Graph regularized Non-negative Matrix …

Apr 22, 2025 · Graph data processing is essential for web-scale applications, including social networks, recommendation systems, and web of things (WoT) systems, where large, sparsely connected …

Jul 7, 2022 · In spectral clustering (SC), the clustering result highly depends on the similarity graph matrix. The k-nearest neighbors graph is a popular method to build the similarity graph matrix with a …

Jul 26, 2025 · A matrix is a two-dimensional data object made of m rows and n columns, therefore having total m x n values. If most of the elements of the matrix have 0 value, then it is called a sparse …

Dec 7, 2022 · Multiple graph and semi-supervision techniques have been successfully introduced into the nonnegative matrix factorization (NMF) model for taking full advantage of the manifold structure …

Mar 15, 2015 · In this paper, we propose a sparseness constraint NMF method, named graph regularized matrix factorization with sparse coding (GRNMF_SC). By combining manifold learning …

Mar 1, 2018 · In this paper, we propose a novel semi-supervised NMF, called Dual-graph regularized Non-negative Matrix Factorization with Sparse and Orthogonal constraints (SODNMF). Dual-graph …

Apr 1, 2025 · To address the above-mentioned issues, this paper proposes a scalable sparse bipartite graph factorization method for multi-view clustering (S 2BGFMC). To further improve efficiency, …

Jul 1, 2024 · In this paper, we propose a novel correntropy based sparse graph regularized nonnegative matrix factorization (RSGNMF) to automatically extract discriminative features for depression …

Oct 29, 2025 · Adjacency Matrix Adjacency List Adjacency Matrix Representation An adjacency matrix is a way of representing a graph as a boolean matrix of (0's and 1's). Let's assume there are n vertices …