The Information Bottleneck : Theory and Applications

By Noam Slonim. 2006

220 citations recorded by the source corpus.

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Citations in the corpus

Supervised Dimensionality Reduction via Distance Correlation Maximization (2016, background): “Another parallel, yet motivating line of work on this topic is the popular “Information Bottleneck Method” introduced and studied in (Tishby Naftali and William, 1999; Chechik Gal and Yair, 2005; Noam, 2002) which tries to find a compressed representation ofX while also preserving relevant informati”
A dependence maximization view of clustering (2007, methodology): “In the case of hard clustering, where each observation is assigned to exactly one cluster, mutual information I(X, Y ) has been used as the objective (Slonim, 2002, Section 4.2).”
Recommender systems based on user reviews: the state of the art (2015, methodology): “All of the pairs’ scores are then used to establish a reviewer-item-aspect matrix, and a soft clustering algorithm called the iterative Information Bottleneck (iIB) (Slonim 2002; Tishby et al. 1999) is applied to cluster the reviewers.”
Relevant sparse codes with variational information bottleneck (2016, methodology): “The IB approach has been used to tackle a wide variety of problems, including filtering, prediction and learning [2-5].”
Learning via Hilbert Space Embedding of Distributions (2007, unknown): “Information Bottleneck We try to recode the observations x into new presentations̃ x, by channel all information through a third variables y [122].”
Improving the quality of predictions using textual information in online user reviews (2013, methodology): “We use the Information Bottleneck (IB) method [30] that assigns a probability to each user to belong to every cluster.”
Multivariate Information Bottleneck (2001, methodology): “It employs a greedy agglomerative clustering technique to find a hierarchical clustering tree in a bottom-up fashion and was found to be useful for various problems (Slonim, 2002).”
Information Bottleneck for Gaussian Variables (2003, background): “…useful for a range of applications from documents clustering (Slonim and Tishby, 2000) through neural code analysis (Dimitrov and Miller, 2001) to gene expression analysis (Friedman et al., 2001; Sinkkonen and Kaski, 2001) (for a more detailed review of IB clustering algorithms see Slonim (2003)).”
Maximally Informative Hierarchical Representations of High-Dimensional Data (2014, unknown): “CO G X H RS CIN F K CV X N FX PCA R A IG BM S TSN EQ T BBY W EC H ES CB LU V EIX M U O I M M C SLB A ET TER FA ST AV P SW K JBL CSC W A G D O W K M B PLD M SFT RRC PX D G W W BW A TA P IFF CM CSA U SB H O T ED JD SU PEG D N B M H FI O RLY ZIO N A O N PN R G A S BFB STT FE BA X L H A S CLF K IM D…”