Agglomerative Multivariate Information Bottleneck
In Neural Information Processing Systems (NIPS 01), 2001.
The Information bottleneck method is an unsupervised
model independent data organization technique. Given a joint
distribution P(A,B), this method constructs a new variable T that
extracts partitions, or clusters, over the values of A that are
informative about B. In a recent paper, we introduced a general
principled framework for multivariate extensions of the information
bottleneck method that allows us to consider multiple systems of data
partitions that are inter-related. In this paper, we present a new family
of simple agglomerative algorithms to construct such systems of
inter-related clusters. We analyze the behavior of these algorithms
and apply them to several real-life datasets.
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