Clustering with Multiple Graphs

By Wei Tang, Zhengdong Lu, Inderjit S. Dhillon. 2009

332 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics

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Citations in the corpus

A Survey on Multi-View Clustering (2017, unknown): “At the same time, MVC papers appear largely in many top venues like the conferences ICML [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], NIPS [19], [20], CVPR [21], [22], [23], [24], ICCV [25], AAAI [26], [27], [28], [29], [30], IJCAI [31], [32], [33], [34], [35], [36]”
On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization (2016, background): “ree categories in accordance with [1]: 1) graph-based approaches, 2) co-training or co-regularized approaches, 3) subspace learning algorithms. The ﬁrst stream is the graph-based approaches [2], [3], [4], [5], [6] which exploit the relationship among different views by using multiple graph fusion st”
Multi-view clustering: A survey (2018, unknown): “1 Graph-based MvC Based on multiple similarity graphs, Tang et al.[59] discussed a general clustering problem.”
Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2, 1 Regularization (2015, background): “Linked Matrix Factorization [33] is proposed to explore clustering of a set of entities given multiple graphs.”
Multimodal Manifold Analysis by Simultaneous Diagonalization of Laplacians (2015, unknown): “In Section 4, we show that many recent works on multi-view clustering [25], [26], [27], [28], [29] and manifold alignment [30], [31] can be considered as particular instances of our framework.”
Low-Rank Tensor Constrained Multiview Subspace Clustering (2015, methodology): “The approach in [35] fuses the information from multiple graphs with Linked Matrix Factorization (LMF).”
Latent Multi-view Subspace Clustering (2017, methodology): “Some methods [20, 24] utilize matrix factorization technique for multi-view clustering.”
GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data (2019, methodology): “problem [35] that minimizes the following objective:”
Generalized Latent Multi-View Subspace Clustering (2020, methodology): “Under a matrix factorization framework, some methods [27], [28] attempt to uncover a common representation to link different views for clustering.”
Diversity-induced Multi-view Subspace Clustering (2015, methodology): “Nevertheless, the main limitation of the existing methods [8, 9, 12, 16, 17, 24, 25] is that they could not guarantee the complementarity across different similarity matrices corresponding to different views.”
Data Fusion by Matrix Factorization (2013, methodology): “analysis [33], combining many interaction networks into a composite network [34], multiple graph clustering with linked matrix factorization [8], a mixture of Markov chains associated with different graphs [35], dependency-seeking clustering algorithms with variational Bayes [36], latent factor anal”
Efficient community detection in large networks using content and links (2012, methodology): “[24] proposed a linked matrix factorization method, where each graph’s adjacency matrix is decomposed into a “characteristic” matrix and a common factor matrix shared among all graphs.”
A Co-training Approach for Multi-view Spectral Clustering (2011, methodology): “In (Tang et al., 2009), the information from multiple graphs are fused using Linked Matrix Factorization.”
Co-regularized Multi-view Spectral Clustering (2011, methodology): “In [22], the information from multiple graphs are fused using Linked Matrix Factorization.”