Efficient Information Theoretic Clustering on Discrete Lattices

TL;DR

This paper introduces an efficient information theoretic clustering method for data on discrete lattices, using convolutions to significantly reduce runtime and bridge machine learning with signal processing.

Contribution

It proposes a novel, computationally efficient clustering algorithm for lattice-structured data by replacing costly steps with convolutions.

Findings

Reduced runtime through convolution-based implementation

Effective clustering on lattice-structured data

Bridging machine learning and signal processing

Abstract

We consider the problem of clustering data that reside on discrete, low dimensional lattices. Canonical examples for this setting are found in image segmentation and key point extraction. Our solution is based on a recent approach to information theoretic clustering where clusters result from an iterative procedure that minimizes a divergence measure. We replace costly processing steps in the original algorithm by means of convolutions. These allow for highly efficient implementations and thus significantly reduce runtime. This paper therefore bridges a gap between machine learning and signal processing.