Efficient Information Theoretic Clustering on Discrete Lattices
This work provides an incremental improvement for researchers and practitioners in machine learning and signal processing dealing with efficient clustering on structured data.
The paper tackles the problem of clustering data on discrete, low-dimensional lattices, such as in image segmentation, by replacing costly steps in an existing information-theoretic algorithm with convolutions, resulting in significantly reduced runtime.
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.