Multi-View Clustering via Joint Nonnegative Matrix Factorization

By Jing Gao, Jiawei Han, Jialu Liu, Chi Wang. SDM, 2013

1,014 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/889272722e8f6fd67ac0991fdb81f4626798adbd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Tat-seng Chua
3. Xiang Wang
4. Liqiang Nie
5. Dacheng Tao
6. Bing Liu
7. Feiping Nie
8. Philip S. Yu

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Ling Shao
3. Tat-seng Chua
4. Luc Van Gool
5. Xiangnan He
6. Yi Yang
7. Ming-Hsuan Yang
8. Xiaogang Wang

Citations in the corpus

• Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (2017, background): “• One reasonable assumption hold by a lot of multi-view clustering research [Gao et al. , 2013; Kumar et al. , 2011; Bickel and Scheffer., 2004; Kumar and Daume, 2011] is that all the views should share the similar underlying clustering structure. i,j ∈ V || U i − U j || 2 F aims to achieve the…”
• A Survey on Multi-View Clustering (2017, positive): “[79] extended nonnegative matrix factorization to multi-view settings for clustering multilingual documents.”
• Multi-view low-rank sparse subspace clustering (2017, methodology): “Depending on the constraints imposed on the low-dimensional representation, this problem can be solved using e.g. Principal Component Analysis (PCA) [ 4], Independent Component Analysis (ICA) [5] or Non-negative Matrix Factorization (NMF) [6, 7, 8].”
• Deep multi-view learning methods: A review (2021, methodology): “For the deep multi-view object recognition methods, we compare the classification accuracy (Accuracy) [49] of the following 660 approaches: multi-view CNN (MVCNN) [49], Kd-tree based network (KD-Net) [51], multi-view harmonized bilinear network (MHBN) [52], group-view CNN (GVCNN) [50] and dynamic ro”
• Comment-based multi-view clustering of web 2.0 items (2014, methodology): “It is most similar in spirit to [1, 32].”
• Multi-level Feature Learning for Contrastive Multi-view Clustering (2021, neutral): “The second category of MVC methods is based on the matrix factorization technique [23,56] that is formally equivalent to the relaxation of K -means [26].”
• Partial Multi-View Clustering (2014, methodology): “Note the second setting is what Piyush et al.(2010) have considered, which is a special case of our partial view setting.”
• Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2, 1 Regularization (2015, methodology): “Recently, [6,27] propsed to use NMF to clustering data from multiple views/sources.”
• Latent Multi-view Subspace Clustering (2017, methodology): “To address the large scale issue, a robust large-scale multi-view clustering method [20] is proposed under the framework of K-means algorithm.”
• Large-Scale Multi-View Spectral Clustering via Bipartite Graph (2015, methodology): “Some previous methods just simply assume equal weights (Kumar, Rai, and Daume 2011) or tuning one parameter for each view (Liu et al. 2013).”
• Multi-omic and multi-view clustering algorithms: review and cancer benchmark (2018, methodology): “MultiNMF (54) uses the following generalization: Each omic Xm is factorized into WmHm.”
• GMC: Graph-Based Multi-View Clustering (2020, methodology): “We compare our method with the following baseline methods: Single view K-means (SK-means), Single viewNormalized cut (SNcut) [60], Multi-view K-means Clustering (MKC) [41], Multi-view clustering via Non-negative Matrix Factorization (MultiNMF) [37], Co-regularized Spectral Clustering (CoregSC) [19],”
• A review on machine learning principles for multi-view biological data integration (2016, methodology): “Similar to iCluster [77], MultiNMF [113] and EquiNMF [114] allow all views to share a unique coefficient matrix for collective clustering, while Joint-NMF [115] restricts all”
• Unifying Virtual and Physical Worlds (2017, methodology): “Since each of the steps in Algorithms 1 and 2 decreases the objective function that has a lower bound of 0, the convergence of the alternating optimization is guaranteed [Gao et al. 2013b].”
• Multi-omics Data Integration, Interpretation, and Its Application (2020, methodology): “In their analysis, rMKL-LPP, MCCA, and multiNMF 78 performed better than other methods in terms of clinical subtype enrichment.”
• Binary Multi-View Clustering (2019, methodology): “Many multi-view clustering methods have been proposed [7], [13], [19], which can be roughly divided into three groups: the first category projects features from different views into a common low-dimensional feature subspace such as using CCA [6] to minimize the cross-correlation error, and then empl”