Collaborative Information Bottleneck
نویسندگان
چکیده
منابع مشابه
Information Bottleneck Co-clustering
Co-clustering has emerged as an important approach for mining contingency data matrices. We present a novel approach to co-clustering based on the Information Bottleneck principle, called Information Bottleneck Co-clustering (IBCC), which supports both soft-partition and hardpartition co-clusterings, and leverages an annealing-style strategy to bypass local optima. Existing co-clustering method...
متن کاملConditional Information Bottleneck Clustering
We present an extension of the well-known information bottleneck framework, called conditional information bottleneck, which takes negative relevance information into account by maximizing a conditional mutual information score. This general approach can be utilized in a data mining context to extract relevant information that is at the same time novel relative to known properties or structures...
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We define the relevant information in a signal x ∈ X as being the information that this signal provides about another signal y ∈ Y . Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken. Understanding the signal x requires more than just predicting y, it also requires specifying wh...
متن کاملMultivariate Information Bottleneck
The information bottleneck (IB) method is an unsupervised model independent data organization technique. Given a joint distribution, p(X, Y), this method constructs a new variable, T, that extracts partitions, or clusters, over the values of X that are informative about Y. Algorithms that are motivated by the IB method have already been applied to text classification, gene expression, neural co...
متن کاملDeep Variational Information Bottleneck
We present a variational approximation to the information bottleneck of Tishby et al. (1999). This variational approach allows us to parameterize the information bottleneck model using a neural network and leverage the reparameterization trick for efficient training. We call this method “Deep Variational Information Bottleneck”, or Deep VIB. We show that models trained with the VIB objective ou...
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ژورنال
عنوان ژورنال: IEEE Transactions on Information Theory
سال: 2019
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2018.2883295