Spectral Clustering with Two Views

By Virginia de Sa. 2007

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Citations in the corpus

The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA (2019, background): “Another related ﬁeld of study is multi-view clustering, which considers a multi-view setting and aims at performing clustering on a given dataset, see e.g. [11] and [25].”
Generalized Latent Multi-View Subspace Clustering (2020, methodology): “One of the early methods presented in [26] focuses on handling two-view data.”
Multi-View Clustering via Deep Matrix Factorization (2017, background): “(6) MinD(isagreement) (de Sa 2005) builds a bipartite graph which derives from the “minimizing-disagreement” idea.”
On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization (2016, methodology): “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 strategy.”
Multi-view learning overview: Recent progress and new challenges (2017, methodology): “Most work was designed based on space transformation methods [77, 91, 92, 93, 94, 95].”
Weakly supervised learning via statistical sufficiency (2016, background): “These are for instance self-training and co-training [Blum and Mitchell, 1998; Nigam and Ghani, 2000], multi-view learning [de Sa, 2005] and graph cuts [Blum and Chawla, 2001] for semi-supervised learning, the framework of collective graphical models [Sheldon and Dietterich, 2011; Bernstein and…”
Multi-Task Multi-View Clustering (2016, methodology): “The multi-view clustering algorithm in [40] based on the “minimizing-disagreement” idea.”
Multi-Task Multi-View Clustering for Non-Negative Data (2015, methodology): “The method in [de Sa, 2005] is based on the minimizingdisagreement idea.”
Multi-view clustering: A survey (2018, unknown): “[19], De Sa developed a spectral clustering algorithm on two independent views, each of which could be fed into a clustering model.”
Consistent and Specific Multi-View Subspace Clustering (2018, methodology): “Min-Disagreement (De Sa 2005) minimizes the disagreement of different views using bipartite graph which is based on spectral clustering algorithm.”
View-invariant human action recognition via robust locally adaptive multi-view learning (2015, background): “Many existing multi-view learning algorithms are based on the spectral analysis theory, which usually needs to build a nearest neighborhood graph to represent the geometric structure of data (de Sa Virginia, 2005; Zhou and Burges, 2007; Xia et al., 2010).”
Kernel-Based Weighted Multi-view Clustering (2012, background): “Due to the discrete nature of Y this becomes a hard optimization problem, but if Y is relaxed to be an arbitrary orthonormal matrix (i.e.”
Partial Multi-View Clustering (2014, methodology): “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…”
A Survey on Multi-View Clustering (2017, unknown): “Based on the idea of “minimizing disagreement” between clusters from each view, De Sa [12] proposes a two-view spectral clustering that creates a bipartite graph of the views.”
Multi-omic and multi-view clustering algorithms: review and cancer benchmark (2018, methodology): “Similarity-based methods Spectral clustering generalizations Generalizations of similarity based spectral clustering to multi-omics data [23, 24, 25, 26] Matlab”
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.”
Sense discovery via co-clustering on images and text (2015, result): “Finally, our work is also closely related to approaches in co-clustering [14, 16] and multi-view clustering based approaches [15].”