Multi-view clustering via canonical correlation analysis

By Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan. 2009

845 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Adversarial Continual Learning (2020, background): “…at either maximizing the mutual agreement on distinct views of the data, or focus on obtaining a latent subspace shared by multiple views by assuming that the input views are generated from this latent subspace using Canonical correlation analysis and clustering [7], Gaussian processes [32], etc.”
A Survey on Multi-view Learning (2013, methodology): “Chaudhuri et al. (2009) exploited CCA to project the data to the subspace spanned by the means, and then applied standard clustering algorithms to this subspace. This subspace is valuable for the subsequent clustering, because, when projected onto this subspace, the means of the distributions are we”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, background): “ons (features) by analyzing the views simultaneously. This makes possible to solve the task when not all the views are available. Such situations appear when processing simultaneously audio and video [92], documents containing both image and text (e.g. web pages or photos with tags or comments) [93,”
Deep Canonical Correlation Analysis (2013, background): “, 2007); learning features for a single view when another view is available for representation learning but not at prediction time (Blaschko & Lampert, 2008; Chaudhuri et al., 2009; Arora & Livescu, 2012); and reducing sample complexity of prediction problems using unlabeled data (Kakade & Foster, 2”
Clustering with Multiple Graphs (2009, background): “Another direction is proposed by Chaudhuri et al [10] where”
A Method of Moments for Mixture Models and Hidden Markov Models (2012, methodology): “Such separation conditions are natural to expect in many clustering applications, and a number of spectral projection techniques have been shown to enhance the separation (Vempala and Wang, 2002; Kannan et al., 2005; Brubaker and Vempala, 2008; Chaudhuri et al., 2009).”
Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2020, methodology): “In the past few years, many multiview clustering methods have been proposed, such as the multiview k-means clustering [17], canonical correlation analysis (CCA)-based method [18], co-regularized multiview spectral clustering [19], low-rank tensor-based method [20], the deep matrix factorization-base”
Deep multi-view learning methods: A review (2021, methodology): “CCA that it usually is utilized to conduct the task of MVL and has obtained promising performance in various tasks including dimension reduction [12, 13, 14], classification [92, 93] and clustering [94, 95], when multiple views are 295 available.”
COMIC: Multi-view Clustering Without Parameter Selection (2019, methodology): “Based on the way to utilize the multi-view information, existing methods could be roughly classified into the following categories, namely, canonical correlation analysis (Chaudhuri et al., 2009), multi-view matrix factorization (Zhang et al.”
Multi-View Clustering via Joint Nonnegative Matrix Factorization (2013, background): “Experimental results on synthetic and several real datasets demonstrate the effectiveness of our approach.”
Multi-View Intact Space Learning (2015, background): “A number of works exploit the shared latent subspace across diverse views, such as canonical correlation analysis (CCA) [28], [29], its kernel extension [30], its probabilistic interpretation [31], and its sparse formulation [32].”
Confusion Matrix Stability Bounds for Multiclass Classification (2012, methodology): “our primary concern — as we shall see, though, getting bounds on the confusion matrix entails, as a byproduct, bounds on the misclassiﬁcation rate. Building on matrix-based concentration inequalities [1,2,3,4,5], also referred to as noncommutative concentration inequalities, we establish a stability”
Multi-view clustering: A survey (2018, neutral): “Assuming that all views are conditionally independent, given the clustering labels, Chaudhuri et al.[97] presented a multi-view subspace learning method based on canonical correlation analysis.”
A Spectral Algorithm for Latent Dirichlet Allocation (2012, methodology): “…and Schulman, 2007; Vempala and Wang, 2002; Kannan et al., 2005; Achlioptas and McSherry, 2005; Chaudhuri and Rao, 2008; Brubaker and Vempala, 2008; Chaudhuri et al., 2009) or removed these conditions (Kalai et al., 2010; Belkin and Sinha, 2010; Moitra and Valiant, 2010); roughly speaking, the…”
Generalized Latent Multi-View Subspace Clustering (2020, methodology): “The first employs CCA to project multiple views onto a low-dimensional subspace and then uses the learned representation for clustering [36].”