A generalized maximum entropy approach to bregman co-clustering and matrix approximation

By Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Srujana Merugu, Dharmendra S. Modha. Journal of machine learning research, 2004

483 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0b68ad65eb35eeb54e858ea427f14985f211a28c

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Regression-based latent factor models (2009, background): “The factors could be discrete [16, 7, 4] or continuous [22, 17], our work falls in the latter category.”
Multi-Task Multi-View Clustering (2016, methodology): “The multi-task Bregman clustering algorithm in [27], [28] alternatively updates the single-task Bregman clustering and learns the relationships between clusters of different tasks, andmakes the two phases boost each other.”
Information Bottleneck Co-clustering (2010, background): “Both methods [2, 16] perform hard-partition co-clustering.”
Biclustering in data mining (2008, methodology): “The following result states an important relation between the loss of information and distribution q(X, Y) [32]: Lemma 1.”
The Discrete Basis Problem (2006, methodology): “Different methods for co-clustering have been proposed, see, for example, work of Banerjee, Dhillon and others [15].”
Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities (2010, background): “The Bregman divergence is a pseudo-distance for measuring discrepancy between two values of density functions p(x) and q(x) [9,16,80]: dΦ(p||q) = Φ(p)− Φ(q)− (p− q)Φ′(q) (52)”
Multi-Task Multi-View Clustering for Non-Negative Data (2015, methodology): “Co-clustering clusters the samples and features simultaneously so that the clustering performance of samples can be improved by the clustering of features, and vice versa [Banerjee et al., 2007].”
A scalable collaborative filtering framework based on co-clustering (2005, methodology): “In this paper, we consider a novel collaborative filtering approach based on a recently proposed weighted co-clustering algorithm [3] that involves simultaneous clustering of users and items.”
A classification for community discovery methods in complex networks (2011, methodology): “In this case, the authors propose to measure it in terms of the approximation error or the expected Bregman distortion [74] between the original tensor and the approximate tensor built after applying the clustering function.”
CopyCatch: stopping group attacks by spotting lockstep behavior in social networks (2013, background): “There has been extensive research on co-clustering including [3] [4] [8] [21], with applications ranging from collaborative filtering [14] to anomaly and intrusion detection [20].”