Multi-view clustering

By Steffen Bickel, Tobias Scheffer. Industrial Conference on Data Mining, 2004

890 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6386b3d3453c81967927863577745733615ecc2e

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In Artificial Intelligence

Citations in the corpus

Multi-view clustering: A survey (2018, positive): “As a result, Bickel and Scheffer[10] concluded that the multi-view EM algorithm significantly outperformed the single-view”
Constrained Semi-supervised Learning in the Presence of Unanticipated Classes (2016, methodology): “Because our multi-view techniques are broadly similar to co-training based algorithms, we also compared them to a seeded version of the multi-view spherical KMeans algorithm that is proposed by Bickel and Scheffer [10].”
Audio-Visual Instance Discrimination with Cross-Modal Agreement (2020, background): “Classical approaches can be broadly categorized in co-training procedures [6, 7, 30, 43, 50, 67, 84] that maximize the mutual agreement between views, multiple kernel learning procedures [5, 40, 44] which use kernels to model different views, and subspace learning procedures [15, 68] which seek to f”
Late Fusion Incomplete Multi-view Clustering (2018, neutral): “MULTI-VIEW clustering (MVC) optimally integrates features from different views to improve clustering performance [1].”
Co-regularized Multi-view Spectral Clustering (2011, methodology): “For example, [19] proposed an Co-EM based framework for multi-view clustering in mixture models.”
On Deep Multi-View Representation Learning (2015, background): “…et al., 2003; Haghighi et al., 2008; Chandar et al., 2014; Faruqui and Dyer, 2014; Lu et al., 2015), but may also be different information extracted from the same source, such as words + context (Pennington et al., 2014) or document text + text of inbound hyperlinks (Bickel and Scheffer, 2004).”
Neighborhood repulsed metric learning for kinship verification (2012, background): “Previous studies have shown that different feature descriptors could provide complementary information in characterizing facial information from different viewpoints [7], [12], [38], [51], [69], [71], [73], and hence it is desirable to utilize multiple feature information for our kinship verificatio”
Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition (2014, methodology): “…quantities of data in multiple representations from diverse sources, multi-view clustering algorithms have attracted considerable attention (Chaudhuri et al. 2009; Bickel and Scheffer 2004; Kumar and Daumé 2011; Kumar, Rai, and Daumé 2011; Zhou and Burges 2007; Greene and Cunningham 2009).”
Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (2017, methodology): “References [15] and [16] perform multiview information incorporation into the clustering process by optimizing certain objective loss function.”
On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization (2016, methodology): “[8] provided a clus tering method by interchanging the partition information among di fferent views.”
A Survey on Multi-View Clustering (2017, neutral): “[56] a reference value β0 is determined with the empirical rule β0 = N2logN/ PN i,j=1dφ(xi,xj)to identify a reasonable range of β. 2) Multi-View Clustering Based on Mixture Models or EM Algorithm: In [58], under the assumption that the two views are independent, multinomial distribution is adopted f”
Low-Rank Tensor Constrained Multiview Subspace Clustering (2015, methodology): “Early methods focus on the setting of “twoview” [14, 1, 6]: The most recent method in [14] constructs a bipartite graph to connect two types of features, and uses a standard spectral clustering algorithm to obtain the final clustering result.”
Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2, 1 Regularization (2015, background): “Multi-view clustering [1,7] provides a natural way for generating clusters from such data.”
Multi-View Intact Space Learning (2015, positive): “Many variants [8], [9], [10], [11], [12], [13], [14], [15], such as co-EM [8] and co-regularization [10], [11], have since been developed.”
Kernel-Based Weighted Multi-view Clustering (2012, methodology): “Our work focuses on multi-view clustering [4], [5], where the absence of a ground-truth to guide the learning process makes the underlining task much harder.”
Subspace clustering (2012, background): “Research on multiview clustering comes from the other directions, 55,80–82 seeking to allow some redundancy at least to not exclude possibly interesting concepts, although they might have a certain (partial) overlap with other concepts.”
A Survey on Multi-view Learning (2013, background): “Co-training (Blum and Mitchell, 1998) is one of the earliest schemes for multi-view learning. It trains alternately to maximize the mutual agreement on two distinct views of the unlabeled data. Many variants have since been developed. Nigam and Ghani (2000) generalized expectation-maximization (EM) ”