Multi-View K-Means Clustering on Big Data

By Xiao Cai, Feiping Nie, Heng Huang. International Joint Conference on Artificial Intelligence, 2013

634 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1479d27df9ae0673165e850b3ab2e987a9e2f62b

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Citations in the corpus

Scalable Multi-view Subspace Clustering with Unified Anchors (2021, methodology): “RMKM[3] (n + h)k O(n) 30,475 MVSC[8] 2vn2 + nk O(n3) 1,230 AMGL[23] vn2 + nk O(n3) 12,613 MLRSSC[2] (v + 1)n2 O(n3) 2,000 FMR[14] n2 + nm O(n3) 10,158 PMSC[10] 2vn2 + (v + 1)nk O(n3) 2,386 MLES[5] n2 + hm +mn O(n3) 544 LMVSC[11] vm(n + h) O(n) 30,000 Ours mn + (h +m)k O(n) 101,499”
Clustering and projected clustering with adaptive neighbors (2014, background): “In the past decades, many clustering algorithms have been proposed, such as hierarchical clustering [10], K-means clustering [13], spectral clustering [15], spectral embedded clustering [19], support vector clustering [1], maximum margin clustering [22], initialization independent clustering [18], m”
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]”
Multi-level Feature Learning for Contrastive Multi-view Clustering (2021, background): “[4] introduced a shared clustering indicator matrix for multiple views and handled a constrained matrix factorization problem.”
Graph Learning for Multiview Clustering (2018, unknown): “Because the data graph and manifold information are utilized in these graph-based clustering methods, they show better performance than multiview k-means clustering [28].”
A review of clustering techniques and developments (2017, background): “Big data [161-168] is also an emerging issue.”
Late Fusion Incomplete Multi-view Clustering (2018, background): “It has been intensively studied during the last few decade [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14] and widely used in various applications, including object segmentation [15], [16], object pose estimation [17], image re-ranking [18], saliency detection [19], information ”
Multi-View Clustering via Deep Matrix Factorization (2017, methodology): “Extensive research efforts have been made in developing effective MVC methods (Cai, Nie, and Huang 2013a; Gao et al. 2015; Xu, Han, and Nie 2016; Zhao, Liu, and Fu 2016).”
Generalized Latent Multi-View Subspace Clustering (2020, methodology): “For large-scale data, a robust, large-scale, multi-view k-means clustering method [31] can be parallelized and run on multi-core processors for large-scale data clustering.”
Constrained Semi-supervised Learning in the Presence of Unanticipated Classes (2016, methodology): “[22] proposed the use of structured sparsity inducing norms to make K-Means algorithm run on large datasets using multi-threaded machines.”
GMC: Graph-Based Multi-View Clustering (2020, methodology): “The other related multi-view clustering methods can be roughly classiﬁed into three categories: co-training style clustering [19], [26], [27], [28], multi-kernel clustering [29], [30], [31], [32], and multi-view subspace clustering [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43].”
Multiview Consensus Graph Clustering (2019, methodology): “clustering to multiview setting directly [31], there are two ways using k-means clustering in multiview learning, one”
Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (2017, methodology): “Upon that, we will also investigate the problem of learning the weight [ Nie et al. , 2017; Nie et al. , 2016a; Cai et al. , 2013 ] for each view.”
Multi-view clustering: A survey (2018, unknown): “Based on G-orthogonal NMF[127], Cai et al.[140] presented a robust multi-view K-means clustering algorithm for large-scale multiview data.”
Binary Multi-View Clustering (2019, methodology): “Moreover, it is common in many real-world applications that data are collected from diverse sources or represented by heterogeneous features from different views [5], [6], [7].”
Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance (2021, unknown): “• Multi-view k-means Clustering on Big Data (RMKM) [46].”
Multi-view learning overview: Recent progress and new challenges (2017, background): “[129] proposed a novel robust large-scale multi-view K-means clustering approach, which can be easily parallelized and performed on multi-core processors for big visual data clustering.”
Multi-view Subspace Clustering (2015, methodology): “To solve the large-scale multi-view clustering problems, [2] proposed multi-view K-means clustering method.”
Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2020, methodology): “, clustering accuracy (ACC), normalized mutual information (NMI), and purity to compare the above methods [17].”