Spectral clustering and transductive learning with multiple views

By Dengyong Zhou, C. Burges. 2007

481 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Consistency of the group Lasso and multiple kernel learning (2007, unknown): “Note that those different data sources are sometimes referred to as views (see, e.g., Zhou and Burges, 2007).”
A Co-training Approach for Multi-view Spectral Clustering (2011, methodology): “In (Zhou & Burges, 2007), the authors obtain a graph cut which is good on average over the multiple graphs but may not be the best for a single graph.”
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).”
Essential Tensor Learning for Multi-View Spectral Clustering (2018, methodology): “Then, [22] constructs a transition probability matrix of Markov chain on each view, and then combines these matrices via a Markov mixture.”
Deep Multimodality Model for Multi-task Multi-view Learning (2019, methodology): “In addition, we can naturally extend this multi-view clustering method to accommodate the scenario where some views may be missing for some tasks as in [27].”
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”
Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding (2015, background): “Some more recently effort on the topic of multiview feature em50 bedding including [22, 23, 24, 25, 26].”
Kernel-Based Weighted Multi-view Clustering (2012, background): “A framework that generalizes the normalized cut to the multi-view case was introduced by Zhou and Burges [16].”
Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation (2014, methodology): “Most solutions are based on the seminal work of Zhou et al [38] which generalises spectral clustering from a single graph to multiple graphs by defining a mixture of random walks on multiple graphs.”
Co-regularized Multi-view Spectral Clustering (2011, background): “Multi-view clustering algorithms have also been proposed i n the framework of spectral clustering [11, 20, 21].”
Partial Multi-View Clustering (2014, background): “The former are extensions from single-view spectral clustering approaches with the help of some similarity measures (de Sa 2005; Zhou and Burges 2007; Kumar and Daumé 2011; Kumar, Rai, and Daumé 2011), whereas the latter generally try to identify a latent subspace across the multiple views (Hardoon ”
Multi-task low-rank affinity pursuit for image segmentation (2011, methodology): “However, this option may not fully capture the advantages of multiple features, because the affinity matrices are still computed from different features individually, and thus the cross-feature information is not well considered during the inference process.”
Multi-omic and multi-view clustering algorithms: review and cancer benchmark (2018, background): “Zhou and Burges (26) views similarity matrices as networks, and examines random walks on these networks.”
Unifying Virtual and Physical Worlds (2017, methodology): “This is inspired by the proverb “a leopard never changes his spots in different environments” and the principle of “neighborhood preserving” [Ronen et al. 2014; Jiang et al. 2014; Nie et al. 2016].”