Geometric Clustering Using the Information Bottleneck Method

We argue that K–means and deterministic annealing algorithms for geometric clustering can be derived from the more general Information Bottleneck approach. If we cluster the identities of data points to preserve information about their location, the set of optimal solutions is massively degenerate. But if we treat the equations that define the optimal solution as an iterative algorithm, then a set of “smooth” initial conditions selects solutions with the desired geometrical properties. In addition to conceptual unification, we argue that this approach can be more efficient and robust than classic algorithms.

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Susanne Still, William Bialek, Léon Bottou
• Keywords: Deterministic Annealing Algorithms, Information Bottleneck Approach, NIPS 2003, NIPS 2007, Optimal Solution